Dr Thomas Bestwick-Stevenson (School of Medicine), Teaching Associate – thomas.bestwick-stevenson@nottingham.ac.uk

Professor Kimberley Edwards (School of Medicine, Professor of Sport, Exercise, and Nutrition Education – kimberley.edwards@nottingham.ac.uk  

This project is not funded, and we are seeking a student who can self-fund the PhD.

Programme description: Athletes, coaches and scientists are constantly looking for methods to improve sporting performances, and a popular method is the use of legal ergogenic aids. However, there are various ergogenic aids, and the amount of knowledge and research into them differs. Thus, the theme of this PhD programme is to examine the effect of an ergogenic aid on sporting performance(s), where there is currently a lack of understanding (whether this be the ergogenic aid or sport which currently has the lack of understanding).

The research team currently works on ongoing studies which examines aspects related to this, thus this PhD may expand on some aspects of these studies.

PhD description: The project could use different methods to address differing aspect of this topic and could be reliant on both the interest of the student and supervisory team, plus what is found through the process of the research. But it is likely the project will involve a review, to gain an understanding into the topic plus a series of studies examining the ergogenic aid.

Further information: Applicants should have at least a 2:1 in a relevant degree (for example but not limited to Sport Science/Medicine/Rehab, Public Health, Medicine), and ideally a relevant Master’s degree (for example but not limited to Sport Science/Medicine/Rehab, Public Health, Medicine).

Informal enquiries may be addressed to Dr Thomas Bestwick-Stevenson, thomas.bestwick-stevenson@nottingham.ac.uk

To apply, candidates should send their CV and a short cover letter (<1000 words) outlining why they are applying to be part of this PhD programme and what they believe they can offer to: thomas.bestwick-stevenson@nottingham.ac.uk. The email subject line should be: “ERGOGENIC AID PHD APPLICATION”. Candidates should also provide the contact details for 2 referees, one of whom should be their most recent academic supervisor (or line manager in relevant employment, if applicable). Please note, offers of study will be subject to 2 satisfactory references being received.

Closing Date for Applications: Monday July 6^th 2026.

Provisional Interview Date: Tuesday July 14^th 2026.

PhD Start Date: 1^st October 2026, or as soon as possible thereafter.

Closing Date: 06 Jul 2026
Category: Studentships

Professor Kimberley Edwards (School of Medicine, Professor of Sport, Exercise, and Nutrition Education – kimberley.edwards@nottingham.ac.uk

Dr Thomas Bestwick-Stevenson (School of Medicine), Teaching Associate – thomas.bestwick-stevenson@nottingham.ac.uk

This project is not funded, and we are seeking a student who can self-fund the PhD.

Programme description: The overall theme of this PhD programme is to examine the contribution of lifestyle factors, especially physical activity, and the interaction with health and modifiable risk factors. This is important because physical activity provides many health benefits, and because of this there are recommendations for the amount of physical activity people should complete. However, there are also negatives to physical activity. One of which is suffering injuries, which can damage joints and lead other conditions. All of which (physical activity, health, and injury) maybe associated with modifiable risk factors, and information related to this could be useful in the development of prevention strategies, but there is limited research examining this currently. Thus, the current project aims to examine different aspects related to this.

The research teams currently works on studies related to these topics (for example a longitudinal cohort study related physical activity, health, and modifiable risk factors), and this PhD aims to use or expand on some aspects of these studies.

PhD description: The project could use different methods to address differing aspect of this topic and could be reliant on both the interest of the student and supervisory team, plus what is found through the process of the research.

Further information: Applicants should have at least a 2:1 in a relevant degree (for example but not limited to Sport Science/Medicine/Rehab, Public Health, Medicine), and ideally a relevant Master’s degree (for example but not limited to Sport Science/Medicine/Rehab, Public Health, Medicine).

Informal enquiries may be addressed to Dr Thomas Bestwick-Stevenson, thomas.bestwick-stevenson@nottingham.ac.uk

To apply, candidates should send their CV and a short cover letter (<1000 words) outlining why they are applying to be part of this PhD programme and what they believe they can offer to: thomas.bestwick-stevenson@nottingham.ac.uk. The email subject line should be: “HEALTH OF ADULTS PHD APPLICATION”. Candidates should also provide the contact details for 2 referees, one of whom should be their most recent academic supervisor (or line manager in relevant employment, if applicable). Please note, offers of study will be subject to 2 satisfactory references being received.

Closing Date for Applications: Monday July 6^th 2026.

Provisional Interview Date: Tuesday July 14^th 2026.

PhD Start Date: 1^st October 2026, or as soon as possible thereafter.

Closing Date: 06 Jul 2026
Category: Studentships

Dr Thomas Bestwick-Stevenson (School of Medicine), Teaching Associate – thomas.bestwick-stevenson@nottingham.ac.uk

Professor Kimberley Edwards (School of Medicine, Professor of Sport, Exercise, and Nutrition Education – kimberley.edwards@nottingham.ac.uk  

This project is not funded, and we are seeking a student who can self-fund the PhD.

Programme description: The overall theme of this PhD programme is to examine risk factors of suffering an ankle sprain and/or recovering poorly from the injury. This is important as ankle sprains are a common injury (in both the general population and physically active population) as well as being one which many people do not recover well from and often suffer a reinjury. Thus, the injury has a high prevalence and can produce lifestyle limiting symptoms, which has an impact on both the individual and health care providers, and can also be a financial burden 

However relatively little is known about risk factors for both suffering the injury and recovering poorly from it. Consequently, this PhD would aim to explore factors related to suffering and/or recovering from ankle sprains.

The research team currently works on ongoing studies which examines aspects related to this (for example the significant ankle ligament injury study). This PhD may use or expand on some aspects of these studies.

PhD description: The project could use different methods to address differing aspect of this topic and could be reliant on both the interest of the student and supervisory team, plus what is found through the process of the research.

Further information: Applicants should have at least a 2:1 in a relevant degree (for example but not limited to Sport Science/Medicine/Rehab, Public Health, Medicine), and ideally a relevant Master’s degree (for example but not limited to Sport Science/Medicine/Rehab, Public Health, Medicine).

Informal enquiries may be addressed to Dr Thomas Bestwick-Stevenson, thomas.bestwick-stevenson@nottingham.ac.uk

To apply, candidates should send their CV and a short cover letter (<1000 words) outlining why they are applying to be part of this PhD programme and what they believe they can offer to: thomas.bestwick-stevenson@nottingham.ac.uk. The email subject line should be: “ANKLE SPRAIN PHD APPLICATION”. Candidates should also provide the contact details for 2 referees, one of whom should be their most recent academic supervisor (or line manager in relevant employment, if applicable). Please note, offers of study will be subject to 2 satisfactory references being received.

Closing Date for Applications: Monday July 6^th 2026.

Provisional Interview Date: Tuesday July 14^th 2026.

PhD Start Date: 1^st October 2026, or as soon as possible thereafter.

Closing Date: 06 Jul 2026
Category: Studentships

Supervisor: Prof Rumiana Ray (University of Nottingham)

Secondary Supervisor: Dr Hadrien Peyret (University of Nottingham), Dr Dong-Hyun Kim (University of Nottingham), Prof Toby Bruce (Keele University)

Subject Area: Plant Health, Food Security, Sustainable Agriculture and Climate Resilience

Research Title: Hidden Pest-Pathogen Alliances: Unravelling the Mechanisms of Aphid-Fungal Cooperation on Wheat

Research Description

Wheat underpins global food security, providing a major source of calories for both human consumption and animal feed. However, crop productivity is increasingly threatened by plant diseases, insect pests, climate change, and growing pressure to reduce chemical inputs. Understanding how multiple biological threats interact within crops represents one of the major challenges facing sustainable agriculture.
 

Plant pathogens and insect herbivores are typically studied in isolation, despite sharing the same host and frequently occurring together in agricultural systems. Emerging evidence suggests that interactions between pests and pathogens can profoundly alter plant health, disease development, and crop productivity. However, the ecological and molecular mechanisms underpinning these interactions remain poorly understood.
 

This fully funded PhD studentship, supported by leading UK agricultural charities, will investigate interactions between the fungal pathogen Fusarium graminearum, the causal agent of Fusarium Head Blight (FHB), and the English grain aphid (Sitobion avenae), one of the most economically important pests of cereal crops. FHB causes substantial losses through yield reduction and contamination of grain with harmful mycotoxins, while aphids impact crop performance through direct feeding damage and modification of plant physiological responses.
 
The project will investigate how fungal infection influences aphid behaviour, how aphids modify disease development, and how wheat responds to simultaneous attack by pests and pathogens. Combining plant pathology, chemical ecology, molecular biology, metabolomics, transcriptomics, bioinformatics, and systems biology, the research will uncover the mechanisms governing interactions within the wheat–aphid–fungus system and identify new opportunities for sustainable crop protection.
 
The student will receive advanced interdisciplinary training in behavioural ecology, fungal biology, analytical chemistry, mass spectrometry, multi-omics data integration, disease epidemiology, and molecular plant-microbe-insect interactions. Research will utilise world-leading facilities at the University of Nottingham, including advanced metabolomics, genomics, controlled-environment facilities, and bioinformatics infrastructure, alongside specialist chemical ecology facilities at Keele University.
 
The supervisory team comprises internationally recognised researchers in plant pathology, insect chemical ecology, analytical bioscience, biotechnology, and bioinformatics. The project offers opportunities to engage with breeders, agronomists, industry partners, and the wider agricultural sector, providing excellent preparation for careers in academia, biotechnology, crop protection, plant breeding, and agri-food innovation.

Keyword Search
 Plant Pathology, Entomology, Chemical Ecology, Molecular Biology, Multi-omics, Systems Biology, Crop Protection, Wheat, Fusarium, Aphids, Sustainable Agriculture, Food Security, Climate Change

Award Start Date: 01/10/2026

Duration of Award: 48 months

Terms and Conditions

This research studentship is only available to UK citizens and includes payment of tuition fees and a tax-free stipend based on current BBSRC rates.

Applicant Qualification Requirements

1st, 2:1 or MSc degree in Plant science, Agriculture, Microbiology, Ecology, Biotechnology, Genetics, Biochemistry, Molecular biology, or related biological sciences

How to Apply

To apply, please submit a CV and a brief statement (maximum two pages) outlining your academic background, research interests, relevant experience, and motivation for undertaking this PhD project to Prof Rumiana Ray - rumiana.ray@nottingham.ac.uk
 
Informal Enquiries by prospective applicants are encouraged. Please contact Prof Rumiana Ray for an informal discussion: Email: rumiana.ray@nottingham.ac.uk. When making an enquiry, please attach a current CV and include a brief summary of your research interests and academic background.

Closing Date: 17/08/2026

Closing Date: 17 Aug 2026
Category: Studentships

Supervisor: Dr Rebecca Ford

Secondary Supervisor: Dr Qian Yang

Subject Area: Sensory and Consumer Science

Research Title: Tea Changes Everything: Understanding the Unique Sensory Experience that Tea Brings to Ready to Drink Tea Beverages

Research Description

Tea is a hero ingredient in iced teas, offering a unique sensory experience. This PhD project will explore the role of tea in Ready To Drink beverages: how tea interacts with other components to shape consumer perception and emotional response. The research will involve the application of sensory methods to systematically evaluate how tea type and flavourings interact to influence taste, flavour and mouthfeel and the mechanisms of these interactions. To capture responses more effectively, liking and emotional response to a range of formulations will be captured from consumers.
 
The findings of the project will be used by the company sponsor to elevate the tea experience for their consumers. The successful candidate will gain multidisciplinary expertise in sensory science, analytical chemistry, product formulation and consumer research. They will work closely with Pepsi-Lipton, contributing to real-world innovation in the global tea beverage market.
Why choose this project?

The successful candidate will join a supportive, interdisciplinary research environment at the Sensory Science Centre (SSC). The SSC conducts world-leading sensory and consumer research to advance sustainable behaviour and the links between perception and food choice. The centre achieves this via its ISO standard sensory/consumer facilities, external expert sensory panel and Connect Nottingham facilities including immersive and observational technology.
 
The successful candidate will integrate with the University’s sensory research teams, participate in bimonthly research meetings, an annual research away day, and access internal training in Sensory Evaluation, Statistical Methods, and Consumer Sensory Science. The research group fosters a collaborative culture, promotes work–life balance via wellness initiatives, and provides conference presentation opportunities and international networking with world leading researchers.

In parallel, the student will get on-site experience at Pepsi-Lipton. With regular supervisory meetings with industry supervisors, the student will develop hands-on experiences with real world application and explore how fundamental research translates into the holistic experience of the product that can be translated by Pepsi-Lipton’s Consumer Technical Insights and Marketing teams.

Keyword Search: Sensory Science, Consumer Science, Tea, Research and Development, Product Formulation

Award Start Date: 01/10/2026

Duration of Award: 48 months

Terms and Conditions

This research studentship is only available to UK citizens and includes payment of tuition fees and a tax-free stipend based on current BBSRC rates, which are set at £21,805 for 2026/27 entry.

Applicant Qualification Requirements

Applications are invited from candidates in Bioscience, Psychology, Biochemistry, Microbiology, Biotechnology, Chemistry, Chemical/Biochemical/Process Engineering, Environmental Science, Engineering, Pharmacy, Computer Science, Maths or related disciplines who have/expect to graduate with a first/upper-second UK honours degree, or equivalent qualifications gained outside the UK. Candidates with a background in food science and/or nutrition, sensory science, or related disciplines and a passion for sensory evaluation, consumer research and analytical chemistry is desirable.

How to Apply

Applications should include a CV and covering letter explaining why you wish to do a PhD and what makes you an ideal candidate for this PhD research in particular. Please send applications to: r.ford@nottingham.ac.uk.

Closing Date: 17/06/2026

Closing Date: 17 Jun 2026
Category: Studentships

Vision

We are seeking a PhD student that is motivated and passionate about the design and control of power electronics technologies that make real-world impact. Together we will make technological advances that bring compact, reliable and economical energy management.

Motivation 

This PhD project focuses on the development of next-generation power electronics in the form of Solid-State Transformers which will provide key functionality in the electricity networks of the future which will feed, for example, high power charging systems and data centres and link renewable energy sources and energy storage elements.

Aim

The aim of the project is to consider the use of modern power electronics in multi-cellular converters to form Solid State Transformer systems. This will require a study of the current state of the art in SST topologies and control before developing new techniques for both to meet the demands of new loads such as high-power EV charging systems and data centres. You will work with Dr. Alan Watson, Dr. Tabish Mir and Prof. Pat Wheeler at University of Nottingham’s Power Electronics and Machines Centre, which is a purpose-built £18M facility at Jubilee Campus. The PEMC institute is globally renowned and one of the leading research entities in its field. The work is also supported by Siemens AG, Germany and will be led at the facility in Erlangen by Dr Gopal Mondal.

Who we are looking for

We are actively looking for candidates with 

• A first-class (UK equivalent) undergraduate degree in Electrical and/or Electronics Engineering.
• A master’s degree in electrical engineering (particularly power electronics and/or electric drives) is desirable (Preferably Distinction (UK equivalent))
• Knowledge of simulation platforms like MATLAB Simulink/PLECS.
• Coding and hardware skills are desirable. 
• Strong analytical/mathematical skills.
• Passion about research and willingness to learn.
• Good presentation, communication and writing skills. 

Funding support

After a suitable candidate is found, funding is then sought from the University of Nottingham as part of a competitive process (this will cover home tuition fees and UKRI stipend plus a £5,500 a year top-up from the industrial partner)

The University actively supports equality, diversity and inclusion and encourages applications from all sections of society.

The Faculty of Engineering provides a thriving working environment for all PhD students (PGRs) creating a strong sense of community across research disciplines. Community and research culture is important to our PGRs and the FoE support this by working closely with our Postgraduate Research Society (PGES) and our PGR Research Group Reps to enhance the research environment for PGRs. PGRs benefit from training through the Researcher Academy’s Training Programme, those based within the Faculty of Engineering have access to bespoke courses developed for Engineering PGRs. including sessions on paper writing, networking and career development after the PhD.

For information on application process please contact Alan Watson – alan.watson@nottingham.ac.uk

Closing Date: 20 Aug 2026
Category: Studentships

Number of scholarships available: 1

Description of award: £11000 per year for 3 years (full-time)

Eligibility and value of the award:

To be eligible to apply for the Scholarship:

The awardee must have an offer for their PhD programme at the University of Nottingham via the University's online application system to commence in 26/27 academic year studying Full-Time.

Home/EU and International students are eligible to apply, but please note that the total value of the award is £11000 per year for 3 years regardless of the residency of the successful applicant. Any award is subject to satisfactory attendance and progress through the degree programme.

For further details on Postgraduate Research Tuition fees please see here

Application:

Please email your application, with the subject 'TRH PhD Scholarship Application', to Mandy Felton (mandy.felton@nottingham.ac.uk).

Please ensure that it contains the following:

• Your name

• The degree programme to which you will apply.

• Confirmation that you will commence study in October 26/27.

• The title of the proposed doctoral research project

• A research proposal of up to 2,000 words (excluding references). The proposal MUST contain a brief overview of your intended research, including references to key literature and an account of what is novel about your project, as well as the methods you plan to use.

• A statement from both members of your proposed supervisory team (oneof whom must be a member of staff from the School of Politics and International Relations) of up to 250 words. This should explain how their expertise is suited to your project.

The deadline for applications is 5pm on 26 June 2026.

Following the deadline, shortlisted candidates will be contacted to arrange an interview, either in person or online.

The successful applicant will be notified by 27 July 2026.

Please note, this scholarship is offered subject to the finalisation of a funding agreement. The award is conditional on that agreement being signed, and we cannot accept liability if funding is delayed or does not proceed. In the unlikely event this happens, we will contact applicants as soon as possible.

The School of Politics and International Relations values equality, diversity and inclusivity. As the holder of an Athena Swan Award, we are committed to offering equal treatment and opportunities to our workforce and student body. As such, we welcome applications from all, regardless of personal characteristics or
background.

Closing Date: 26 Jun 2026
Category: Studentships

Location: Faculty of Engineering and Faculty of Science (Psychology), University of Nottingham, UK

Start Date: October 2026   

This PhD offers an exciting opportunity to explore ultrathin metamaterials: a novel type of device that utilises digital and mathematical techniques to design multifunctional visual aids to help address and correct diseases of the eye.

You will work at the intersection of mathematics, physics, AI, and clinical practice through careful design and production of optical metasurfaces, which can help to correct macular degeneration and other eyesight problems through careful control of light.

Why apply for this PhD?

• Work on the next-generation optical physics using metamaterials
• Gain a unique combination of skills in mathematics, machine learning, photonics, and clinical practices in vision.
• Be part of a multidisciplinary research team spanning science and engineering, psychology, and healthcare.
• Access state-of-the-art laboratories and cleanroom facilities.
• Gain experience by attending international conferences and training events.
• Develop skills highly valued in both academia and industry. 

Project description

Vision technology relies on careful use of optical components such as lenses. Undoubtedly, standard prescription lenses have been revolutionary in helping billions of people and their quality of life through helping to see more clearly. However, optical technologies are based on standard glass lenses and components which are bulky and have limited capabilities. 

Age-related macular degeneration (AMD) affects around 196 million people worldwide and is a leading cause of central vision loss. It reduces the ability to read, recognise faces, and perform everyday tasks, with limited treatment options available for most patients. Existing assistive technologies rely heavily on digital image processing or bulky external devices, which can be expensive, inconvenient, and inaccessible – where simple prescription lenses simply cannot address this.

This project explores a new approach using optical metasurfaces —ultra-thin optical layers that shape light—to enhance vision directly, without electronics. The aim is to increase contrast at object edges, helping users distinguish shapes and details more clearly. While edge enhancement has been shown to improve visual performance in low-vision patients, it is currently achieved using digital systems. This PhD project translates the principle into a compact, passive optical solution.

The project will combine: 

• Mathematical modelling and simulation of optical/photonic structures and devices
• Fabrication of ultrathin metasurfaces using the University of Nottingham cleanrooms
• Clinical applications through visual neuroscience approaches

Facilities and research environment:

• Photonics and visual neuroscience laboratories;
• Dedicated simulation and modelling softwares for electromagnetic and optical design;
• Access to dedicated cleanroom fabrication facilities;
• A collaborative research environment across psychology and engineering

Candidate profile

You do not need experience in all the areas below; additional training will be provided. Enthusiasm and willingness to learn are essential.

Essential:

1. A 2:1 undergraduate degree or a Master’s degree in Physics, Applied Physics, Mathematical Sciences, computer science, vision science or a closely related subject from a recognised institution.
2. A background in at least one of the following:

• Photonics/Electromagnetics theory, design and simulations
• Nanoscience
• Visual neuroscience or opthalmology

1. Programming skills (Python, MATLAB, or similar)
2. Strong analytical and problem-solving skills.
3. Good written and spoken English.

Desirable:

• Experience with photonic/electromagnetics simulation software.
• Familiarity with deep learning platforms (e.g. TensorFlow, PyTorch), Machine-learning mathematics and algorithms.
• Experience in Imaging systems (e.g. microscopy), and optical laboratory experiments (lasers/lenses)

Funding and eligibility

Open to UK, EU and international students who can provide their own funding capability. 

How to apply

Please apply online. For any enquiries about the project, email Dr Mitchell Kenney at Mitchell.kenney@nottingham.ac.uk or Prof. Paul McGraw at paul.mcgraw@nottingham.ac.uk.

Shortlisted candidates will be invited for an interview to assess their suitability.

Closing Date: 19 Aug 2026
Category: Studentships

High powered lasers are not routinely linked with the circular economy, however intelligent application of these highly controllable and flexible materials processing systems has great potential to advance the move towards a circular economy.

Two distinct aspects are expected to be included in the project, though there is scope to expand to other areas and to adjust the balance between topics depending on the candidate's specific interest and in light of results obtained during the project:

1. disassembly and reuse of end of life composite components
2. recycling of high value waste as feedstock in laser cladding

Previous work has successfully demonstrated laser cutting of carbon fibre composites, CFRP,  this project explores how this process can be exploited in end of life disassembly. The contactless nature of laser processing means that laser systems are highly flexible, different materials and component geometries are accommodated by simply reprogramming the laser path and processing parameters meaning one laser cutting system can disassemble any component geometry. The ability of the same laser to cut through both fibre reinforced composites and metal enables multi material assemblies to be processed, a key requirement.

Multiple advanced manufacturing processes make use of metallic powder based feedstocks. The materials used tend to be inherently expensive, with the need to use them in powder form further adding to that expense. This project will explore new strategies for using recycled feedstock in laser cladding. These include, but are not limited to, collection and reuse of powder, blending recycled and virgin powder, as well as repurposing of machining scrap and waste wire as feedstock, building on existing proof of concept work.

This largely experimental PhD will provide transferable materials characterisation skills, a grounding in advanced manufacturing techniques and direct experience in waste reduction and circular economy principles. This project directly benefits from our recently upgraded laser materials processing facilities as well as the universities extensive suite of materials characterisation equipment. This PhD is expected to produce a larger than average number of journal publications.

Candidate requirements 

You must be a university graduate, or be expecting to graduate, with a 2.1 (or international equivalent) and / or a masters at merit level or above in a relevant subject (engineering, physics, or materials science or closely related disciplines).

Funding

This is a self-funded PhD opportunity therefore you must secure your own funding for both fees and maintenance  either privately or via a scholarship from external/government funding bodies.

Eligibility and how to apply

Open to UK and international candidates.

This PhD project is open until filled. To apply please email Dr Katy Voisey at katy.voisey@nottingham.ac.uk attaching a cover letter, CV and academic transcripts. 

Closing Date: 19 Aug 2026
Category: Studentships

ABA cladding is a variant of laser cladding that was recently developed at The University of Nottingham. By generating clad coatings by first depositing parallel but separated "A" clads and then filling in the valleys formed with "B" clads we have already demonstrated improved material deposition efficiencies compared to conventional cladding. 

This project will expand understanding of the full potential of ABA cladding. There are many aspects that can be explored, and these can be tailored according to the specific interests of the successful candidate. Potential areas of work include:

• multi-material clads, where the A clads are formed from a different material to the B clads
• control of final surface topography
• generation of functionally graded coatings
• the inclusion of pre-placed elements
• development of a process model

This project directly benefits from our recently upgraded laser materials processing facilities as well as the universities extensive suite of materials characterisation equipment. This largely experimental PhD will provide transferable materials characterisation skills, including optical and scanning electron microscopy. The successful candidate will also learn advanced communication skills via preparing and presenting their work at both academic conferences and in journal publications. This PhD is expected to produce a larger than average number of journal publications.

Candidate requirements 

You must be a university graduate, or be expecting to graduate, with a 2.1 (or international equivalent) and / or a masters at merit level or above in a relevant subject (engineering, physics, or materials science or closely related disciplines).

Funding

This is a self-funded PhD opportunity therefore you must secure your own funding for both fees and maintenance either privately or via a scholarship from external/government funding bodies.

Eligibility and how to apply

Open to UK and international candidates.

This PhD project is open until filled. To apply please email Dr Katy Voisey at katy.voisey@nottingham.ac.uk attaching a cover letter, CV and academic transcripts. 

Closing Date: 19 Aug 2026
Category: Studentships

The increasing use of multilayer materials and mixed-fibre textiles has created significant challenges for recycling, as these materials are difficult to separate yet retain valuable functional properties such as flexibility, durability, and water resistance. As a result, large volumes are currently downcycled or sent to landfill.

This PhD addresses the lack of systematic approaches for identifying and repurposing such complex waste streams. The project will focus on understanding the relationships between material composition, structure, and residual properties, and how these can be exploited in alternative applications.

The research will combine detailed materials characterisation (e.g. microscopy, compositional and structural analysis) with the development of frameworks for classifying and matching waste materials to viable reuse pathways. In parallel, the project will explore constraints on implementation, including material variability, supply consistency, and user behaviour, incorporating insights from survey data and textual analysis.

By integrating technical and socio-economic perspectives, the project aims to develop new strategies for the valorisation of complex waste streams that are currently considered unrecyclable.

The successful candidate will gain experience in advanced materials characterisation, interdisciplinary research design, and both quantitative and qualitative data analysis, with opportunities to contribute to publications in sustainable materials and circular economy research.    

Candidate requirements 

You must be a university graduate, or be expecting to graduate, with a 2.1 (or international equivalent) and / or a masters at merit level or above in a relevant subject (engineering, physics, or materials science or closely related disciplines). The work will include consideration of public perceptions and behaviour, hence an interest in economics and/or psychology would be an advantage. The successful candidate will be expected to go out and about to directly engage with a variety of different relevant parties, making communication skills important.

Funding

This is a self-funded PhD opportunity therefore you must secure your own funding for both fees and maintenance either privately or via a scholarship from external/government funding bodies.

Eligibility and how to apply

Open to UK and international candidates.

This PhD project is open until filled. To apply please email Dr Katy Voisey at katy.voisey@nottingham.ac.uk attaching a cover letter, CV and academic transcripts. 

Closing Date: 19 Aug 2026
Category: Studentships

The research programme will use a mixture of computational, analytical and machine learning approaches to model the heat transfer to fuels and their physical and chemical behaviour, including changes in chemistry and physical properties. The interaction between fuel chemistry and physical behaviour will be investigated. If appropriate experimental analysis to provide validation data will be acquired as part of the PhD, although where possible validation data will be taken from industrial and openly available literature.  The successful candidate will gain experience in computational, analytical and experimental approaches across mechanical and chemical engineering, applied in an aerospace industry context.

The successful candidate will be based in the Mechanical and Aerospace Systems research group (previously known as G2TRC) within the Faculty of Engineering and will be part of a supportive team of 50 researchers, technicians, support staff and academics. The group has a dynamic research culture with a programme of seminars, writing and social events, with a research office hub providing a quiet working environment with social and meeting spaces.

We are looking for an enthusiastic and self-motivated person with a rigorous approach to research. Applicants should have or be expected to gain a high 2:1, preferably a 1st class honours degree in Chemical or Mechanical or Aerospace Engineering or Chemistry or Computer Science a related degree. A good knowledge and/or experience in heat transfer is essential, as is the ability to work well in a team. Prior experience in the areas of computational fluid dynamics, chemistry, machine learning or computational heat transfer will be an advantage.

The successful applicant would be expected to spend part of the PhD period based in Bristol at the Airbus site and will receive supervision support and training from both the University and Airbus.  This research will support the path to net zero flights and there may be opportunities to become involved in practical aspects of fuel system design and testing during the PhD.

The PhD studentship will cover fees and tax free stipend of £24,000 p.a. for 4 years. Due to funding restrictions this studentship is only available to UK (home fees) citizens.  

Informal enquiries may be addressed to Prof. Carol Eastwick, carol.eastwick@nottingham.ac.uk 

Interested in this studentship? Applications with a CV, cover letter and academic transcripts should be sent to hadrian.moran@nottingham.ac.uk 

Suitable applicants will be interviewed, and if successful, invited to make a formal application. 

Closing Date: 31 Jul 2026
Category: Studentships

Applications are invited for a fully-funded Industrial Doctoral Landscape Award, offered in partnership with Rolls-Royce, to tackle key challenges in the design of aeroengine oil systems using multiphase Computational Fluid Dynamics (CFD). This is an exciting opportunity to contribute to cutting-edge research that supports the next generation of  sustainable aeroengines.

The successful candidate will join a supportive team of 50 researchers, technicians and academics within the Mechanical and Aerospace Systems Research Group, and will have the opportunity to apply their research during a placement within Rolls Royce.

Project Overview

The project focuses on developing and applying advanced CFD models for aeroengine oil systems. There will also be opportunities to integrate machine learning techniques for building lower-order predictive models. The student will gain hands-on experience in industrial applications, including practical aspects of aeroengine oil system design, spending part of their PhD based on-site at Rolls-Royce as well as receiving joint supervision and training from both the University and industry professionals.

Candidate Requirements

We are seeking an enthusiastic, self-motivated researcher with a rigorous approach to problem-solving. Applicants should have, or be expected to gain, a high 2:1 (preferably 1st class) honours degree in Mechanical or Aerospace Engineering, or a related discipline with substantial background in fluid mechanics.

Essential skills:

• Strong knowledge of numerical methods
• Ability to work effectively in a team

Desirable skills / experience:

• Experience of applying CFD to a complex problem
• Knowledge of multiphase flows
• Experience with machine learning techniques

Funding

This studentship covers UK home tuition fees and provides a tax-free stipend of up to £25,000 per year for 4 years. Please note that, due to funding restrictions, this studentship is only available to UK (home fees) citizens.

Start date – 1 October 2026

Application Process

Informal enquiries may be addressed to:
Dr Stephen Ambrose – Stephen.Ambrose3@nottingham.ac.uk  or

Dr Chris Ellis – Chris.Ellis@nottingham.ac.uk

Interested candidates should submit the following documents:

• Curriculum Vitae (CV)
• Cover letter
• Academic transcripts

Applications should be sent to IAT@nottingham.ac.uk

Candidates will be interviewed at the earliest possible convenience, and the position will close once a suitable candidate is found

Closing Date: 24 Jul 2026
Category: Studentships

Start date: 1 October 2026

Application deadline: 24/07/2026

Project type: Collaborative PhD studentship (joint Academic-Industry)

Industrial partner: BAE Systems plc       

Academic supervisor: Dr Sendy Phang, Dr. George Gordon and Dr Alexander Turner 

Industry supervisor: Dr Hassan Zaidi

We are seeking a Ph.D. student to develop next-generation AI systems for real-time 3D mapping on compact, low-power devices. The project will combine optical sensing, event-based vision, and radio-frequency (RF) data with advanced AI to build robust mapping systems for challenging environments, including poor visibility and GPS-denied settings.

This is a joint project with BAE Systems plc, offering access to industrially relevant datasets, equipment, and evaluation scenarios alongside academic research training. It would suit candidates interested in careers in academia or industry, especially in AI, sensing, autonomy, robotics, or embedded systems.

Background

Accurate 3D mapping is increasingly important for autonomy, navigation, inspection, and situational awareness across defence and other safety-critical applications. Yet many real-world deployments cannot depend on cloud computing or high-bandwidth communications. Instead, sensing and AI inference must operate directly at the edge, under tight constraints on power, bandwidth, and compute. This studentship addresses that challenge by developing a multimodal sensing and inference framework that can run on compact AI edge hardware while remaining reliable in complex, contested, or visually degraded environments.

Aim

You will design, build, and evaluate a hardware-aware AI framework for cognitive 3D mapping. The work will bring together three complementary sensing streams:

• structured illumination for active optical depth recovery and high-precision 3D sensing;
• event-based vision for low-latency, high-dynamic-range perception with reduced data rates;
• RF sensing and localisation, spanning radar-style observables and passive RF localisation using software-defined radio.

A central theme of the project is co-design across sensing, AI reconstruction, and embedded deployment. You will explore how multimodal models can generate consistent 3D scene representations with quantified uncertainty, and how these can be deployed efficiently on edge accelerators such as NVIDIA Jetson, Edge TPU, or neuromorphic hardware.

What we offer

Joining our team means gaining access to exceptional resources and opportunities to develop you into a leading researcher:

• A world-class research environment spanning research environment, spanning sensing, nanotechnology, AI, and clinical medicine
• A supportive and inclusive research culture, underpinned by the Researcher Development Concordat (http://www.vitae.ac.uk/policy/concordat).
• Close technical supervision from both academic and industrial partners to work on a real-world industry problem
• Excellent opportunities to publish in leading journals and conferences, and to present your work internationally and travel to conferences.
• Four years of funding, including tuition fees and stipend at the standard rate for eligible UK students.
• Consumables budget for purchasing state-of-the-art edge AI compute units and sensors.
• A project environment well suited to students interested in careers in academia, advanced R&D, or industry innovation.

What you should have

We are seeking a motivated candidate with the enthusiasm and technical foundation to contribute to ambitious interdisciplinary research. You should ideally have:

• A first-class or upper second-class degree, or a master’s degree, in Engineering, Computer Science, Physics, Mathematics, Robotics, or a related discipline.
• A strong interest in one or more of the following areas: AI and machine learning, computer vision, signal processing, sensing, robotics, or embedded systems.
• Programming experience in at least one language such as Python, MATLAB, or C/C++.
• Strong analytical, quantitative, and problem-solving skills.
• The ability to work effectively both independently and as part of a multidisciplinary academic–industry team.
• Eligibility for Home fee status.

Project environment

The project will be based in the Faculty of Engineering at the University of Nottingham, with Dr. Sendy Phang and Dr. George Gordon as the academic supervisors. The student will benefit from a research culture that combines hands-on systems development with advanced AI methods, alongside co-supervision and strategic input from BAE Systems through industry supervisor Dr Hassan Zaidi.

How to apply

Start date: 1 October 2026. For informal enquiries and details on how to apply, please contact Dr Sendy Phang at sendy.phang@nottingham.ac.uk with your CV, a cover letter outlining your research interests and motivation to do this PhD project, and all academic transcripts and any publications.

Closing Date: 24 Jul 2026
Category: Studentships

Funded by the Nuclear Decommissioning Authority, we endeavour to make technological advances with real national impact.

The UK holds significant volumes of nuclear graphite waste, and disposal options are currently limited pending the Geological Disposal Facility (GDF) opening after 2050. New technologies are needed to manage graphite – a key enabler for the dismantling of the first and second generation of UK Nuclear Reactors. Applied smouldering offers a promising solution to reduce the amount of material destined for the GDF: it is energy‑efficient, cost‑effective, and well‑suited to low‑volatility carbon‑based materials.

You will design and conduct laboratory experiments to assess graphite smoulderability, develop physics-based models to predict scalability, and perform techno‑economic analyses and life‑cycle assessments using machine-learning tools. This project will prepare you for starting a career in nuclear decommissioning or applying emerging technological and modelling approaches to facilitate circular economy innovation in the energy transition.

You will work closely with Tarek Rashwan, Oliver Fisher and Rachel L Gomes based in  the Food Water Waste Research Group in the Faculty of Engineering, which leads research in circular economy innovations. You will also liaise extensively with Nuclear Restoration Services, including a multi-month internship, and the Nuclear Decommissioning Authority alongside a broader team of UK academics and industry partners from Canada addressing challenges with nuclear graphite.

Candidate requirements 

Essential:

• 1^st or 2:1 in Engineering or a science-related discipline.
• Strong analytical and problem‑solving skills.

• Enthusiastic, self-motivated, resourceful, and strong willingness to learn.

Desirable:

Previous experimental and/or modelling experience with thermal treatment or combustion/smouldering is an advantage. Full research training will be provided.

Eligibility and funding 

This studentship is open to UK/home and international candidates. For funding reasons, we are particularly looking for UK applicants

PhD start date: October 2026

How to apply

Application deadline: June 1, 2026

To apply, please email your CV and supporting statement explaining your suitability for this PhD position and why you are interested to Dr Tarek Rashwan at tarek.rashwan@nottingham.ac.uk

The University of Nottingham actively supports equality, diversity and inclusion and encourages applications from all sections of society. We - the Faculty of Engineering - provide a thriving working environment for all our postgraduate researchers (PGRs) creating a strong sense of community across research disciplines. We understand that research culture is important to our PGRs so we work closely with our Postgraduate Engineering Society and PGR research group representatives to support and enhance the postgraduate research environment.

As a PGR at the University of Nottingham you will benefit from training through our Researcher Academy’s training programme. Based within the Faculty of Engineering you will have additional access to courses developed specifically for our engineering and architecture PGRs including sessions on how to write a paper, communicating your research, and research integrity. 

We offer dedicated postgraduate study spaces, have outstanding research facilities and work in partnership with leading industrial partners.

Closing Date: 31 Jul 2026
Category: Studentships

Start Date: 1 October 2026   

This PhD offers an exciting opportunity to explore reservoir computing, a new approach towards artificial intelligence that uses the natural dynamic behaviour of physical systems (such as light and electronics) to process information efficiently.

You will work at the intersection of mathematics, physics, electrical engineering and AI, helping to develop a theory that explains how and why these systems work — and how to design better ones. 

Why apply for this PhD?

• Work on the next-generation AI hardware beyond traditional computing architectures. 
• Gain a unique combination of skills in mathematics, machine learning, and photonics.
• Be part of a multidisciplinary research team spanning science and engineering.
• Access state-of-the-art laboratories and high-performance computing facilities. 

• Gain experience by attending international conferences and training events.
• Develop skills highly valued in both academia and industry.

Project description

Modern AI computing systems require large amounts of energy and computational power. Reservoir computing offers a promising alternative by using complex physical systems to perform tasks such as prediction, classification, and signal processing.

However, one major challenge remains: We still do not fully understand what makes a reservoir computing system perform well.

This PhD project aims to answer this question.

You will develop a unified mathematical theory and framework to study and explain how different reservoir systems work and how to design them for specific tasks. The project will combine:

1. Mathematical modelling of dynamical systems;
2. Computational photonics simulations;
3. Comparison with real physical systems (especially photonic systems using light).

Facilities and research environment:

1. High-performance computing facilities;
2. Photonics and electromagnetics laboratories;
3. Experimental platforms for optical (light-based) computing;
4. A collaborative research environment across mathematics and engineering.

Candidate profile

You do not need experience in all the areas below; additional training will be provided. Enthusiasm and willingness to learn are essential.

Essential:

1. A first-class undergraduate degree or a master’s degree in Physics, Applied Physics, Electrical and Electronic Engineering, Mathematical Sciences, or a closely related subject from a recognised institution.
2. A background in at least one of the following:
3. Dynamical systems
4. Photonics/Electromagnetics theory, design and simulations
5. Machine learning mathematics and algorithms
6. Numerical methods
7. Programming skills (Python, MATLAB, or similar)
8. Strong analytical and problem-solving skills.
9. Good written and spoken English.

Desirable:

• Experience with photonic/electromagnetics design software.
• Familiarity with deep learning platforms (e.g. TensorFlow, PyTorch).

Funding and eligibility

The project is fully funded by DSTL, due to funding requirement this studentship is only available for UK (home) candidates.

An UKRI rate studentship is available for this project, covering home tuition fees plus a tax-free stipend. 

How to apply

Send the following documents to sendy.phang@nottingham.ac.uk

1. CV
2. Cover letter explaining your research interests, relevant skills and experience, and why you are interested in this PhD project
3. Academic transcripts (for both undergraduate and postgraduate degrees, if applicable)
4. Copies of any publications (if applicable) 

Please use “PhD-RC-Framework application – [Your Full Name]” as email subject matter.

Shortlisted candidates will be invited for an interview to assess their suitability. 

Supervisors:

Professor Gregor Tanner – School of Mathematical Sciences, gregor.tanner@nottingham.ac.uk 

Dr Sendy Phang – Faculty of Engineering, sendy.phang@nottingham.ac.uk

Closing Date: 22 Jul 2026
Category: Studentships

Enhanced Stipend PhD Studentship (UK) funded by the UK government

Thermally Sprayed Coatings for ablation and high heat flux conditions

Background

UK applicants are invited to undertake a 3-4 year, fully-funded PhD studentship (fees and enhanced stipend) within the Centre of Excellence in Coatings and Surface Engineering (CE-CSE) at the University of Nottingham, funded by the UK government. There is a critical need to develop materials and coatings that can withstand ultra-high temperature (UHT) conditions while maintaining structural integrity and functional performance. 

The PhD Project

This exciting research project is actively seeking ultra-high temperature (UHT) ceramic materials capable of surviving short-duration exposure (on the order of seconds to minutes) under extreme conditions. These environments are characterised by temperatures up to 3000 K, pressures up to 10 MPa, mass fluxes up to 6500 kg/m²·s (including particulate fluxes up to 300 kg/m²·s), gas velocities up to 1000 m/s, and heat transfer coefficients up to 35,000 W/m²·K. Under such conditions, conventional ceramic materials undergo rapid degradation through oxidation, particulate erosion, thermal shock, and phase instability, significantly limiting their performance and service life.

This PhD project will focus on the design and development of UHT ceramics in the form of coatings, ablation and high-heat-flux testing rigs, and characterisation using secondary electron imaging, X-ray diffractometry, electron backscattered diffraction, transmission electron microscopy, and Raman spectroscopy. This is a hugely exciting project for an enthusiastic researcher who wishes to forge an academic or industry career in the materials sector. 

Qualification:

This position will only cover home/UK tuition fees. The candidate must have at least an equivalent of a UK 2.1 class degree in materials/mechanical/chemical/physics/chemistry, or any related discipline. This is an experimental research project, and the candidate is expected to spend the majority of the time at the University of Nottingham.

Funding: 

The PhD studentship will cover full home/UK University tuition fees and a tax-free stipend of up to £27 k per annum for the duration of the project. 

Applications, with a detailed CV and a cover letter, together with the names and addresses of two referees, should be sent directly to Prof. Tanvir Hussain (tanvir.hussain@nottingham.ac.uk).  

Closing date: Until Filled

Closing Date: 15 Jul 2026
Category: Studentships

Applications are invited for an EPSRC Industrial Doctoral Landscape Awards (IDLA) PhD position at the University of Nottingham addressing the specific engineering details of the wear of articulating splines for aeroengine applications.  The successful candidate will have a first-class or upper second-class honours degree in mechanical engineering or a related subject.

This studentship will attract a stipend up to £25,000 per annum for four years. The position arises from a long-standing engineering research relationship between the University of Nottingham and Rolls-Royce plc. Nottingham’s UTC in Gas Turbine Transmissions Systems will host this studentship and the candidate will sit within a community of PhD students at various stages of their study.

Spline couplings are key power-transmission components which allow torque to be transmitted between two shafts while also allowing for assembly/disassembly.  Building on a long history of work within the Transmissions UTC into the performance of spline couplings, this project will seek to further the fundamental understanding the wear behaviour of such components through both experimental and numerical studies.  Experimental work will be carried out using a recently commissioned rig facility in the UTC allowing the validation of modelling tools.

This project has applications in creating more power dense systems which will facilitate increased use and efficiency of high power electrical systems, and also conventional mechanical power offtakes. Reducing the size and weight of these systems, while boosting power extraction is important to continuing to improve the efficiency of aeroengines

This project is available from 1st October 2026. Applications accepted until post is filled.  Informal inquiries can be made via email to Prof. Chris Bennett (c.bennett@nottingham.ac.uk).

Eligibility: Due to funding restrictions this position is only available to UK candidates.

Closing Date: 17 Jun 2026
Category: Studentships

Supervised by Rundong Yan, Alistair Speidel, and Rasa Remenyte-Prescott 

This exciting opportunity is based within the Resilience Engineering Research Group at Faculty of Engineering which conducts cutting edge research into developing modelling techniques to predict ways of improving the design, maintenance, and operation of engineering systems in order to reduce the frequency and consequences of failure.

Vision

We are seeking a PhD student who is motivated to rethink how manufacturing systems are designed, moving beyond forward, trial-and-error approaches towards goal-driven, performance-led system design. The student will work at the intersection of systems engineering, modelling and simulation, and data-driven methods to develop an inverse design framework for manufacturing systems.

Together, we will advance the capability to design manufacturing systems that embed reliability, resilience, adaptability, and sustainability from the outset. By scientifically linking high-level performance objectives to system architecture and design decisions, this research aims to reduce costly late-stage redesign and enable manufacturing systems that can respond effectively to changing operational conditions. The outcomes of this work will support more efficient industrial design processes and contribute to the development of future manufacturing systems that are robust, reconfigurable, and fit for long-term operation.

Motivation 

Modern manufacturing systems are required to operate under increasing uncertainty, frequent change, and competing performance demands, including reliability, resilience, adaptability, and sustainability. However, current manufacturing system design approaches largely remain forward-driven: systems are designed, analysed, and only then assessed against these performance. At the same time, manufacturing is undergoing a major transformation driven by digitalisation, reconfigurable production, and the need for more sustainable and resilient operations. These trends demand design methodologies that can explicitly account for performance goals from the outset, rather than treating them as afterthoughts. Despite advances in modelling, simulation, and data-driven optimisation, there is currently limited methodological support for systematically translating high-level performance objectives into concrete manufacturing system design decisions.

There is a clear need for new design approaches that enable engineers to reason backwards from desired system behaviour to feasible and robust system configurations across different operating environments and requirements. Addressing this gap will support the development of manufacturing systems that can better adapt to change, reduce costly redesign, and deliver sustained performance over their operational lifetime.

Aim

You will have the opportunity to develop a model-based systems engineering framework for the inverse design of manufacturing systems, enabling high-level performance objectives to directly inform system architecture and design decisions.

During the project, you will work closely with academic supervisors from both the Resilience Engineering Research Group and the Advanced Manufacturing Technology Research Group at the University of Nottingham, applying modelling, simulation, and data-driven methods to link high-level performance objectives to practical manufacturing system designs. You will develop and use advanced techniques, such as Petri nets and AI-based optimisation, to explore system behaviour and generate robust, adaptable, and sustainable manufacturing system configurations.

The project will involve applying these approaches to realistic manufacturing environments, allowing you to contribute to both methodological advances and industrially relevant case studies. This experience will prepare you for careers in advanced manufacturing, systems engineering, digital manufacturing, and research roles in academia or industry.

Who we are looking for

We are looking for an enthusiastic, self-motivated, and resourceful candidate with a strong interest in systems engineering, manufacturing systems, and modelling and simulation. You should be able to work independently as well as collaboratively, and be motivated to tackle open-ended research problems.

You should hold, or expect to obtain, a first-class or upper second-class (2:1) degree in a relevant discipline in engineering, science, or mathematics. Experience with modelling, simulation, optimisation, or programming (e.g. Python, MATLAB, C++, or similar) would be advantageous, though not essential, as learning and training will be expected during the PhD study.

Funding support

After a suitable candidate is found, funding is then sought from the University of Nottingham as part of a competitive process. (this will cover home tuition fees and UKRI stipend).

The University actively supports equality, diversity and inclusion and encourages applications from all sections of society

The Faculty of Engineering provides a thriving working environment for all PGRs creating a strong sense of community across research disciplines. Community and research culture is important to our PGRs and the FoE support this by working closely with our Postgraduate Research Society (PGES) and our PGR Research Group Reps to enhance the research environment for PGRs. PGRs benefit from training through the Researcher Academy’s Training Programme, those based within the Faculty of Engineering have access to bespoke courses developed for Engineering PGRs. including sessions on paper writing, networking and career development after the PhD. The Faculty has outstanding facilities and works in partnership with leading industrial partners. 

For any enquiries about the project and the funding, please email Dr Rundong (Derek) Yan (rundong.yan@nottingham.ac.uk), Dr Alistair Speidel (Alistair.Speidel@nottingham.ac.uk), or Dr Rasa Remenyte-Prescott (r.remenyte-prescott@nottingham.ac.uk)

This studentship is open until filled. Early application is strongly encouraged.

Closing Date: 02 Feb 2026
Category: Studentships

This exciting opportunity is based within the Buildings, Energy and Environment (BEE) Research Group in the Faculty of Engineering. The BEE Research Group conducts cutting-edge research into low-energy buildings, building performance, retrofit and decarbonisation, indoor environmental quality, and climate-resilient design, providing a strong interdisciplinary environment for doctoral research.

Vision

This project aims to transform the UK school estate by developing evidence-based, climate-resilient retrofit strategies that deliver healthier indoor environments, lower carbon emissions, and long-term building performance. By integrating Passive House and EnerPHit principles with real building data, the research will support the creation of future-ready schools that protect children’s wellbeing while contributing to national net-zero and climate adaptation goals.

Motivation

This project is aimed at a highly motivated PhD student with an interest in sustainable buildings, retrofit, and environmental performance, who is keen to work with real buildings, performance data, and applied research challenges. The successful candidate will be curious, analytical, and motivated to tackle real-world problems at the intersection of energy, health, and climate resilience.

The research will make a significant societal and environmental impact by addressing one of the most under-researched yet socially critical building types in the UK: schools. Many UK schools suffer from poor energy performance, overheating, inadequate ventilation, and moisture risks, directly affecting children’s health, wellbeing, and learning outcomes. This PhD will develop evidence-based, Passive House–informed retrofit strategies tailored to diverse school typologies, supporting healthier indoor environments, reduced carbon emissions, and long-term resilience. The outcomes will provide practical guidance for designers, policymakers, and school estate managers, contributing to the Net Zero Schools agenda and improving everyday learning environments for future generations.

Aim

You will have the opportunity to develop an evidence-based, Passive House–informed retrofit framework for UK school buildings, focusing on energy efficiency, indoor environmental quality, and climate resilience. You will gain hands-on experience in building performance evaluation, hygrothermal analysis, whole-life carbon assessment, and in-situ environmental monitoring, working with real school buildings and measured datasets. The research aims to deliver practical, scalable retrofit solutions that support healthier learning environments and national net-zero ambitions.

You will work with an experienced and supportive supervisory team within the Buildings, Energy and Environment (BEE) Research Group in the Faculty of Engineering. The project will be led by Dr Sara Mohamed, with co-supervision from academic colleagues within the BEE Research Group. You will also engage with advanced research facilities, real building datasets, and—where appropriate—industry partners and external stakeholders, developing skills relevant to both academic and professional practice.

 Who we are looking for

We are seeking an enthusiastic, self-motivated, and resourceful PhD candidate with a strong interest in sustainable buildings, retrofit, and environmental performance. The successful applicant will be motivated to address real-world challenges related to energy efficiency, indoor environmental quality, and climate resilience, particularly in educational buildings.

Who We Are Looking For

We are seeking an enthusiastic, self-motivated, and resourceful PhD candidate with a strong interest in sustainable buildings, retrofit, and environmental performance. The successful applicant will be motivated to address real-world challenges related to energy efficiency, indoor environmental quality, and climate resilience, particularly in educational buildings.

Essential Competences

The ideal candidate will demonstrate:

• Excellent verbal and written communication skills
• A high level of independence and self-motivation
• An analytical mindset with strong problem-solving abilities
• Strong organisational and time-management skills
• Ability to work effectively both independently and within a research team

Desirable Competences

The prospective candidate may also have:

• A background in architecture or interdisciplinary built-environment fields
• Experience in sustainable architecture or building physics
• A strong interest in retrofit research
• Confidence in using quantitative methods, including environmental monitoring and performance evaluation
• Experience or interest in dynamic building performance analysis
• Ability to collaborate and engage with a range of stakeholders, including academic, industry, and user groups
• Strong analytical skills and the ability to handle data confidently and ethically

Entry Requirements

A first-class or 2:1 undergraduate degree (or equivalent) in Architecture, Architectural Engineering, Building Services Engineering, Environmental Engineering, or a related field.

A relevant Master’s degree, or equivalent professional experience, in sustainable design, building physics, energy modelling, or environmental performance is highly desirable.

Funding Support and Research Environment

After a suitable candidate is identified, funding will be sought from the University of Nottingham as part of a competitive process, covering home tuition fees and a UKRI doctoral stipend.

The University of Nottingham actively supports Equality, Diversity, and Inclusion and encourages applications from all sections of society. The Faculty of Engineering provides a thriving research environment for postgraduate researchers, fostering a strong sense of community across disciplines. PGRs benefit from training through the Researcher Academy Training Programme, including bespoke courses for Engineering researchers on academic writing, networking, and career development. The faculty also offers outstanding facilities and maintains strong partnerships with leading industrial collaborators.

Funding support

After a suitable candidate is found, funding is then sought from the University of Nottingham as part of a competitive process (this will cover home tuition fees and UKRI stipend).

The University actively supports equality, diversity and inclusion and encourages from all sections of society.

The Faculty of Engineering provides a thriving working environment for all PGRs creating a strong sense of community across research disciplines. Community and research culture is important to our PGRs and the FoE support this by working closely with our Postgraduate Research Society (PGES) and our PGR Research Group Reps to enhance the research environment for PGRs. PGRs benefit from training through the Researcher Academy’s Training Programme, those based within the Faculty of Engineering have access to bespoke courses developed for Engineering PGRs. including sessions on paper writing, networking and career development after the PhD. The Faculty has outstanding facilities and works in partnership with leading industrial partners. 

Please contact Sara Mohamed with your CV and supporting statement to apply for this project - sara.mohamed3@nottingham.ac.uk

Closing Date: 02 Feb 2026
Category: Studentships

This exciting opportunity is based within the thriving Optics and Photonics Research Group in Faculty of Engineering which conducts cutting edge research spanning exploration to translation, with curiosity driven projects all the way through to application in the clinic.   

Vision

We are seeking PhD student that is motivated and enthusiastic and keen to push the boundaries of what is currently possible when imaging with an optical microscope. Combing the latest in optical developments with the recent surge in AI, this project aims image the centre of a live intact root for the first time. Something that is currently not possible.

Motivation 

This project will address a long-standing issue in plant biology: the inability to image the centre of live, intact, plant roots. The ability to observe dynamic cellular processes at the centre of a live root for the first time will unlock entirely new lines of biological inquiry, crucial for areas such as sustainable agriculture and food security. Such an imaging system would allow for studies of a plant’s resilience to drought, salinity, and water logging, as well as responses to fungal infections and nanoparticle uptake. It is very common that new optical microscopy techniques are developed to image mammalian tissue, and that these approaches are very slow to translate across to plant biosciences where the impact could be huge and as a result exciting opportunities get missed.  

When we use light to image deep into complex samples there is a common problem that occurs – the light gets distorted and scattered by the structures present in the sample and as a result a nice quality focus and hence a nice image cannot be produced at depth into the sample. At Nottingham we have been working on this problem for several years and have developed methods that shape the incoming light with the equal but opposite distortion to that imposed by the sample to produce a high-quality image deep into the sample of interest. Recently we have been using AI and machine learning to predict the distortion present and significantly speed up this correction process.

This PhD project will take the latest in AI-informed wavefront correction techniques and tailor them to imaging deep into plant roots. It will use a range of state-of-the-art optical microscopes based in the Optics and Photonics Research Group in the Faculty of Engineering, plus those housed in Plant Biosciences at the Sutton Bonnington campus. Data sets will be generated using simulated and experimental data and these will be used to train networks to predict the common distortions that occur when imaging into plant roots. From here we can either correct for these distortions using the hardware in the microscope or in software using reconstruction algorithms. This is an exciting multidisciplinary PhD project that promises to make cutting-edge advances in all research areas involved.

Aim

This project combines practical hands-on optics experimentation with training neural networks to develop the next generation of optical microscopes. You will have the opportunity gain skills in optical instrumentation and imaging, AI and machine learning, and in plant biology and sample handling.

Your base will be in the Optics and Photonics Group in the Faculty of Engineering and from here you will work with a team of academics and researchers across Engineering, Computer Science and the Biosciences.

You will be supervised by Amanda Wright (Optics and Photonics Research Group, Faculty of Engineering), Mike Somekh (Optics and Photonics Research Group, Faculty of Engineering), Mike Pound (Computer Vision, Computer Science Department), and Darren Wells (Plant and Crop Biophysics, School of Biosciences).

Who we are looking for

An enthusiastic, self-motivated, resourceful student, who likes working as part of a team and is keen to take on a new challenge. An understanding of optics and/or machine learning is desirable but not essential, along with general coding skills.

1^st or a 2:1 in a relevant field (for example Physics, Electrical and Electronic Engineering, Computer Science, or Biosciences).

Funding support

After a suitable candidate is found, funding is then sought from the University of Nottingham as part of a competitive process (this will cover home tuition fees and UKRI stipend)

The University actively supports equality, diversity and inclusion and encourages applications from all sections of society.

The Faculty of Engineering provides a thriving working environment for all PGRs creating a strong sense of community across research disciplines. Community and research culture is important to our PGRs and the FoE support this by working closely with our Postgraduate Research Society (PGES) and our PGR Research Group Reps to enhance the research environment for PGRs. PGRs benefit from training through the Researcher Academy’s Training Programme, those based within the Faculty of Engineering have access to bespoke courses developed for Engineering PGRs. including sessions on paper writing, networking and career development after the PhD. The Faculty has outstanding facilities and works in partnership with leading industrial partners. 

Please contact Amanda Wright with your CV and supporting statement to apply for this project – amanda.wright@nottingham.ac.uk 

Closing Date: 02 Feb 2026
Category: Studentships

This exciting opportunity is based within the Advanced Manufacturing Technology Research Group at Faculty of Engineering which conducts cutting edge research into sustainable high-value manufacturing processes.

Vision

We are looking for a PhD student who is motivated to develop the next generation of manufacturing processes alongside our partners in Rolls-Royce.

Aviation faces a dual challenge: decarbonisation and growing vulnerability in critical raw material supply chains. High-temperature aerospace components rely on exotic alloys and coatings with high embodied carbon and zero domestic supply, yet these components degrade in service.

This PhD project is driven by a vision of extending the life, performance, and value of existing aerospace assets, reducing reliance on virgin critical materials, and enabling more sustainable and circular manufacturing practices within the aerospace sector.

Motivation 

For aerospace gas turbines, most emissions occur during operation, but the materials used to manufacture critical components also carry a significant environmental and strategic burden. During service, components such as blades, guide vanes, and compressors are damaged by calcia–magnesia–alumino–silicate (CMAS) ingress, which degrades thermal barrier coatings and limits component life.

Current recoating and preventative coating methods are effective at a bulk level but struggle to preserve or restore small-scale engineered features that are essential for thermal and aerodynamic performance. This creates a strong need for precision, adaptable, and scalable reconditioning approaches that go beyond conventional manufacturing routes.

Aim

The aim of this PhD is to develop and understand non-conventional electrophysical and laser-based manufacturing processes for the restoration and remanufacturing of aerospace gas turbine components.

The project will:

• Investigate fundamental process–material interactions between coatings, substrates, and electrophysical/laser processes
• Explore process-specific phenomena (including plasma effects) to enable highly localised material removal and deposition
• Develop best-practice methodologies for restoring or enhancing small-scale functional features
• Translate findings towards scalable and deployable solutions, with miniaturised, on-wing demonstration

The research will be conducted in close collaboration with Rolls-Royce and will directly inform industrial practice in component repair and life-extension.

Who we are looking for

We are seeking a highly motivated and curious PhD candidate with a strong interest in advanced manufacturing, materials, and sustainability. You should have (or expect to obtain) a good first degree (1^st or a 2:1) in a relevant discipline, such as:

• Mechanical Engineering
• Manufacturing Engineering
• Materials Science/Metallurgy

The ideal candidate will:

• Enjoy hands-on research
• Be interested in non-conventional manufacturing processes (e.g. EDM, laser processing, coatings)
• Be motivated by industry-focused research with real-world impact
• Be comfortable working at the interface of academia and industry

You will join a supportive supervisory team spanning academic and industrial expertise, with access to specialist equipment (including EDM and laser systems) and strong links to Rolls-Royce and university spin-outs.

Please contact Alistair Speidel for further questions and to apply for this opportunity alistair.speidel@nottingham.ac.uk

Funding support

After a suitable candidate is found, funding is then sought from the University of Nottingham as part of a competitive process (this will cover home tuition fees and UKRI stipend)

The University actively supports equality, diversity and inclusion and encourages applications from all sections of society.

The Faculty of Engineering provides a thriving working environment for all PGRs creating a strong sense of community across research disciplines. Community and research culture is important to our PGRs and the FoE support this by working closely with our Postgraduate Research Society (PGES) and our PGR Research Group Reps to enhance the research environment for PGRs. PGRs benefit from training through the Researcher Academy’s Training Programme, those based within the Faculty of Engineering have access to bespoke courses developed for Engineering PGRs. including sessions on paper writing, networking and career development after the PhD. The Faculty has outstanding facilities and works in partnership with leading industrial partners. 

Closing Date: 02 Feb 2026
Category: Studentships

Location: Nottingham

Funding For: UK Students 

Funding amount: Full tuition fee waiver pa (Home Students only) and stipend at above UKRI rates pa (currently at £20,780 for 2025/26 academic year, increasing in line with inflation). Research training and support grant (RTSG) of £3000 per year. Funding is available for 4 years.

Hours: Full Time

Closes: Open until position filled

The overarching aim of this project is to find synergies between methods and ideas of modern machine learning and of statistical mechanics for the study of stochastic dynamics with application to the analysis of time series. In particular, the project will examine and develop methods that go beyond the Markovian paradigm. It will consider a range of time series data, focusing on those that show challenging properties of uncertainty, irregularity and mixed-modality. It will examine a range of models and techniques that go beyond Markovian approaches, including state-space models, tensor networks, and machine learning frameworks such as recurrent neural networks and transformers. Models and datasets will be studied and benchmarked in key tasks relating to both prediction/forecasting and anomaly detection. Comparison with known analytic methods and established Markov models will be made wherever possible. Expected outcomes include a unified non-Markovian framework for time series analysis, a suite of relevant datasets, and large-scale statistical studies comparing different methods. The successful candidate will be jointly supervised by:

Dr Edward Gillman (https://www.nottingham.ac.uk/physics/people/edward.gillman)

and

Professor Juan P. Garrahan (https://www.nottingham.ac.uk/physics/people/juan.garrahan)

Supervisors: Dr Edward Gillman, Professor Juan P. Garrahan

Entry requirements

Open to UK nationals only (This placement will require national security vetting at the security check (SC) level, which makes the restriction to UK nationals necessary). Expected starting date October 2025. We are seeking candidates with:

• Relevant subject matter experience at required level (e.g. 2.1 or above undergraduate degree in physics, mathematics or computer science)

• Willingness to adapt and work across different disciplines

• Ability to work independently and cooperatively

• Commitment to inclusivity, responsible research and innovation

How to apply

Applications should be submitted by following the steps outlined on the page https://www.nottingham.ac.uk/physics/studywithus/postgraduate/howtoapply.aspx

In the “Research Proposal Section” of the online application simply state that you are applying to the open position on “Machine Learning for Probabilistic Modelling” with Dr Edward Gillman and Professor Juan P. Garrahan as supervisors.

Funding Fully and directly funded for this project only. Full tuition fee waiver p.a. (Home Students only) and stipend at above UKRI rates p.a. (currently at £20,780 for 2025/26academic year, increasing in line with inflation). Funding is available for 4 years

Application deadline: Open until the position is filled

Enquiries: Contact Dr Edward Gillman (edward.gillman@nottingham.ac.uk)

Closing Date: 25 Jul 2025
Category: Studentships