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 and Dr. George Gordon

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: 01 Jun 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

Funding duration: 36 months

Application deadline: midday Friday 22^nd May 2026

Interviews: Week commencing 8^th June 2026

Number of awards: Two fully funded studentships

Overview

The University of Nottingham invites applications for two fully funded PhD studentships within the fields of mental health and neurosciences, commencing 1^st October 2026.

These studentships form an institutionally funded commitment to the Midlands Mental Health and Neurosciences Doctoral Training Partnership (Midlands MHN DTP). The DTP’s vision is to improve mental health across the Midlands through outstanding research, collaboration, and innovation.

Learn more about our research themes and approaches at midlandsmhndtp.ac.uk 

We welcome applications for:

Self-proposed projects within the remit of our highlighted research themes and approaches (see midlandsmhndtp.ac.uk) OR

Defined projects supervised by University of Nottingham, School of Medicine academics (details below)

Projects should align with the DTP’s mission and demonstrate potential to deliver meaningful benefits to mental health in the Midlands.

Funding

This studentship is fully funded by the University of Nottingham for a fixed period of three years, subject to satisfactory academic progress and continued registration.

The funding package comprises:

• Home-rate tuition fees for the full duration of the PhD (three years)
• A research training and support grant, contributing towards project-related research costs and approved personal and professional development activities
• A full-time salaried studentship, paid over three years 

The University’s intention is to align the studentship salary as closely as possible with the applicant’s current healthcare professional role, recognising existing skills, training and clinical experience. However, this alignment is subject to the limits of the available funding, and salary levels are capped accordingly.

As this studentship is supported from a fixed funding allocation, the salary is set at appointment and will remain constant for the duration of the award. The funding does not include incremental pay progression or enhanced employment benefits beyond statutory entitlements.

Two funded positions are available under the same funding arrangement at salary level appointed at up to University of Nottingham R&T spine point 35 or Clinical Doctor in Training spine point 02.

Please note that the salary level is non-incremental and funding cannot be extended beyond the three-year period. Additional benefits such as enhanced family leave pay or discretionary allowances are not included within this funding package.

Eligibility

To be eligible to apply, candidates must:

• Meet the University’s standard PhD entry requirements:
• https://www.nottingham.ac.uk/pgstudy/how-to-apply/research.aspx#check 
• Be a practising healthcare professional, registered with a recognised professional regulatory body (for example, NMC, HCPC, GMC, or GPhC).
• Be classed as a Home student for tuition fee purposes.

How to Apply

Applicants may propose their own research project within the broad areas of mental health and neurosciences or apply to one of the predefined projects (see attached document).

Closing Date: 22 May 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: 01 Jun 2026
Category: Studentships

This studentship is fully funded for 3.5 years from 1^st October 2026. (Home applicants only).

In this Ph.D. project, you will advance this research field by investigating how to develop, design, and evaluate domain specific multi-agentic AI models and systems that can plan and execute tasks with multi-modal heterogeneous data (e.g. text, location, and images), associated with diverse applications, such as earth observation, climate, and phenotyping. Developed models will be tested for a variety of highly relevant problems in agriculture, like crop type classification, crop yield forecasting, field boundary delineation, crop disease, and crop failure detection. The Ph.D. research builds upon recent advancements in multi-agentic AI systems. Processing and integrating multiple data modalities will also be key to the research objective of developing dynamic and intelligent systems that provide further insight into modern agricultural applications and food security problems.

You will work with an interdisciplinary and international team of experts in artificial intelligence (e.g. computer vision, deep learning, AI) and green life sciences (e.g., remote sensing, crop modelling, and food security), within the European funded project AgriscienceFM (Horizon programme), which has recently been awarded by the European Commission. For information about this project can be found here: https://www.agriscience.fm 

Your duties and responsibilities: 

• Familiarise with the state-of-the-art in multi-agentic AI, and how to interact with external models and tools.
• Design, develop, and evaluate multi-agentic AI model architectures to gain and improve our insights in agriculture from analysing multi-modal data.
• Use Anthropic and/or OpenAI APIs.
• Perform large-scale training and testing on an HPC server.
• Disseminate the research results by writing papers and presenting your work at international conferences.
• Collaborate with other project partners in joint tasks, and contribute to the overall project success.

You will be supervised by Valerio Giuffrida (see email below) and one other member of the academic staff within CVL.

What are we looking for?

You are highly motivated, self-driven, and curious to advance use-inspired artificial intelligence methods. You bring along your enthusiasm to work in a highly dynamic, international team towards a common objective. 

In addition: 

• A successfully completed BSc/MSc degree in computer science, artificial intelligence or engineering, or a similar relevant field.
• Proficiency in programming in Python and experience in PyTorch, Scikit-Learn or related modern machine learning libraries.
• Some working knowledge of using Anthropic/OpenAI APIs.
• Good writing skills, or contributions to scientific papers.

Funding

Annual tax-free stipend based on the UKRI rate (£21,805 for 2026/27) plus fully-funded Home PhD tuition fees for the 3.5 years.

Entry Requirements

2:1 Bachelor or Masters degree or international equivalent in computer science, artificial intelligence, or engineering (or related discipline). Studentships are open to home students only. 

Application Process

Applications to be informally made direct to the Valerio Giuffrida Valerio Giuffrida at valerio.giuffrida@nottingham.ac.uk first.

Post interview, application to be made through the MyNottingham system stating the supervisor’s name and project title. The deadline to have completed and submitted your formal application is Friday 29^th May 2026. 

Enquiries to be directed to: Valerio Giuffrida - valerio.giuffrida@nottingham.ac.uk

Closing Date: 29 May 2026
Category: Studentships

Subject Area: Plant & Crop Science

Research Title: Root oxygen dynamics and development

The student will be part of a multidisciplinary effort to investigate the anatomical, physical and cellular factors that shape internal root environments. The project will explore how root organisation and environmental conditions combine to influence oxygen availability, and how these internal conditions vary across space and time. Depending on the student’s interests and skills, the project may involve a combination of:
 • Experimental studies using model plant species to examine root oxygen status under contrasting environmental conditions.
 • Application of imaging- and sensor-based approaches to visualise and quantify oxygen dynamics in roots and their surrounding environment.
 • Analysis of how root tissue organisation and cellular connectivity influence internal microenvironments.
 • Integration of experimental observations with quantitative or computational frameworks, developed in collaboration with partners with modelling expertise.
 This PhD offers the opportunity to work at the interface of plant physiology, root biology, imaging and quantitative analysis. The student’s work will provide key conceptual and experimental foundations that support other strands of the BreathingUnderground project, while allowing scope to develop independent questions within the broader theme.

Award Start Date: 01/10/2026

Duration of Award: 48 months

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

Applicants should be highly motivated, curious and keen to develop expertise at the interface of plant biology and quantitative analysis. Candidates should a Master’s degree, in Plant Science, Biology or a closely related discipline.
Experience or interest in one or more of the following areas would be advantageous (full training will be provided as required):
 • Plant physiology, root biology or plant–environment interactions.
 • Experimental approaches to studying internal plant environments or spatially structured biological processes.
 • Imaging, sensor-based measurements or quantitative data analysis.
 • Basic computational or programming skills, or a willingness to engage with modelling and data-driven approaches.
 Strong communication skills, enthusiasm for interdisciplinary research, and a commitment to rigorous and reproducible science are essential.

Previous applicants need not apply.

How to Apply

Prospective applicants are strongly encouraged to get in touch to discuss the project and their suitability. Informal enquiries may be addressed to vinay.shukla@nottingham.ac.uk. Applications should be submitted by emailing a detailed CV and cover letter to vinay.shukla@nottingham.ac.uk by the stated closing date.
 A complete application should include:
 A detailed CV, clearly outlining academic background, research experience and technical skills. Applicants are encouraged to highlight:
 o Relevant coursework, research projects, or thesis work.
 o Experience with experimental plant biology, physiology, imaging, sensors, or quantitative analysis (where applicable).
 o Any computational, data analysis or programming experience, including software or languages used.
 o Publications, preprints, conference presentations or posters (if available).
 o Names and contact details of two academic referees.
 A cover letter (typically 1–2 pages) explaining:
 o Motivation for applying to this PhD project.
 o Relevant experience and skills, and how these align with the project.
 o Research interests and career aspirations.
 o Why you are interested in working within the BreathingUnderground programme and at the University of Nottingham.

 Keyword Search
Root biology, oxygen dynamics, hypoxia, plant physiology, root development, imaging and biosensors, quantitative analysis, plant–environment interactions

Closing Date: 13 May 2026
Category: Studentships

This project offers an exciting opportunity to undertake industrially linked research with engineering teams of the Manufacturing Technology Centre (MTC) and academics within the Power Electronics, Machines and Control (PEMC) Research Institute, University of Nottingham. The project will be supported by the state-of-the-art electric motor manufacturing platforms at both locations.

Project Description

Electrification is a main enabler for decarbonised transportation. Ambitious roadmaps to achieve the “Net Zero” target by 2050 in the UK require step-change performance of electrical motors from a state-of-the-art continuous power density of 2-5 kW/kg to 10-25 kW/kg by 2035. Incremental improvements in electrical machines built from simple stacks of 2D laminations will not suffice to bridge the power density gap required for next generation electric vehicle traction or aerospace propulsion. A radical approach to how electrical machines can be designed and built with 3D architectures that enable significantly boosted electromagnetic, mechanical and thermal performance is yet to be developed.

The project will motivate the PhD student to revolutionise electrical machine design and development based on programmable 3D electrical steel technology enabled by advanced manufacturing processes and emerging magnetic materials for applications across automotive, aerospace, and power generation. Starting from modelling and parametric design of complex 3D laminated and hybrid cores, the PhD student will design and develop new motor topologies and experimentally characterise their magnetic, mechanical and thermal performance. The optimised design for manufacturing workflow will be demonstrated on application-relevant prototypes, evidencing improvements in power density, efficiency and manufacturability over conventional 2D solutions.

Funding:

• A three-year fully funded studentship
• A generous tax-free annual stipend of £25,000 plus full-time home tuition fees paid.
• An additional £2,000 per annum for consumables and travel.

Requirements: 

• The candidate should have a 1st or high 2:1 degree in electrical/mechanical engineering, physics, mathematics, or related scientific disciplines.
• Skills in numerical tools and programming are desirable (MATLAB, python, C++ etc).
• Any experience or capabilities in engineering design or manufacturing methods would be advantageous.

Eligibility and Application

• Due to funding restrictions, the position is only available for UK home candidates.
• As sponsored by MTC, the successful candidate would need to pass the sponsors own security checks before starting the PhD.
• Start date: 10 April 2026
• Closing date: 15 May 2026

For further information please email Professor Chris Gerada (University of Nottingham) and Will Pollitt (MTC).

Facilities

The MTC is an independent Research and Technology Organisation aimed at de-risking and accelerating the adoption of disruptive technologies within the UK manufacturing sphere. Supported by the UK government, the MTC works closely with industrial partners and other research organisations to deliver world leading innovation across all levels of the UK’s industrial landscape, from SMEs and start-ups to OEMs and large-scale global manufacturers.

The PEMC Institute is home to Driving the Electric Revolution Midlands Industrialisation Centre and the UK Electrification of Aerospace Propulsion Facilities, which have received over £20m of funding in the last three years. This 5000m² institute with state-of-the-art facilities for research into electrification technologies, hosting 21 academics, 60 post-doctoral researchers and over 80 PhD students, will be made available for this project. The university actively supports equality, diversity and inclusion and encourages applications from all sections of society.  

Closing Date: 15 May 2026
Category: Studentships

This project offers an exciting opportunity to undertake industrially linked research with engineering teams of the Manufacturing Technology Centre (MTC) and academics within the Power Electronics, Machines and Control (PEMC) Research Institute, University of Nottingham. The project will be supported by the state-of-the-art electric motor manufacturing platforms at both locations.

Project Description

Electrification is a main enabler for decarbonised transportation. Ambitious roadmaps to achieve the “Net Zero” target by 2050 in the UK require step-change performance of electrical motors from a state-of-the-art continuous power density of 2-5 kW/kg to 10-25 kW/kg by 2035. The highest power dense motors today rely on unsustainable materials and on carbon-intensive manufacturing processes. Incremental improvements in electrical motor technologies will not suffice to bridge the power density gap required for aerospace propulsion, nor sustain the widespread adoption of electrical vehicles in an environmentally friendly and ethical way. A radical approach to how electrical motors is developed, combined with emerging material technology, is needed.

The project will motivate the PhD student to develop next generation electric motors with advanced CNT windings for electric vehicle traction and aerospace propulsion, featuring improved performance, sustainability, and cost-effectiveness. It will start with capability characterisation of the emerging CNT wire technology. After quantifying the superior properties of CNT against copper and aluminium windings in specific high-performance applications, the PhD work will be focused on developing novel motor topologies featuring CNT windings, including designing and testing of optimised prototypes for validation. 

Funding:

• A three-year fully funded studentship
• A generous tax-free annual stipend of £25,000 plus payment of their full-time home tuition fees
• An additional £2,000 per annum for consumables and travel.

Requirements: 

• The candidate should have a 1st or high 2:1 degree in electrical/mechanical engineering, physics, mathematics, or related scientific disciplines.
• Skills in numerical tools and programming are desirable (MATLAB, python, C++ etc).
• Any experience or capabilities in engineering design or manufacturing methods would be advantageous.

Eligibility and Application

• Due to funding restrictions, the position is only available for UK home candidates.
• As sponsored by MTC, the successful candidate would need to pass the sponsors own security checks before starting the PhD.
• Start date: 5 October 2026 
• Closing date: 15 May 2026

For further information please email Professor Chris Gerada (University of Nottingham) and Dan Walton (MTC).

Facilities

The MTC is an independent Research and Technology Organisation aimed at de-risking and accelerating the adoption of disruptive technologies within the UK manufacturing sphere. Supported by the UK government, the MTC works closely with industrial partners and other research organisations to deliver world leading innovation across all levels of the UK’s industrial landscape, from SMEs and start-ups to OEMs and large-scale global manufacturers.

The PEMC Institute is home to Driving the Electric Revolution Midlands Industrialisation Centre and the UK Electrification of Aerospace Propulsion Facilities, which have received over £20m of funding in the last three years. This 5000m² institute with state-of-the-art facilities for research into electrification technologies, hosting 21 academics, 60 post-doctoral researchers and over 80 PhD students, will be made available for this project. The university actively supports equality, diversity and inclusion and encourages applications from all sections of society.  

Closing Date: 15 May 2026
Category: Studentships

University of Nottingham in collaboration with SHD Composites 

Start date: 1 October 2026

The University of Nottingham is seeking an outstanding and highly motivated candidate for a fully funded PhD studentship focused on the development of next‑generation bio‑based composite materials. This exciting project is delivered in partnership with SHD Composite Materials Ltd, a leading UK prepreg manufacturer, and offers a unique opportunity to work at the interface of advanced materials engineering, sustainable manufacturing, and industrial innovation.

Poly‑furfuryl alcohol (PFA) resins, derived from agricultural by‑products, are emerging as one of the most promising sustainable alternatives to conventional epoxy systems. Their excellent thermal stability and favourable fire, smoke and toxicity performance make them strong candidates for safety‑critical applications in aerospace, rail, automotive and battery technologies. However, current PFA systems suffer from brittleness, moisture‑related defects and narrow processing windows, limiting their wider adoption. This PhD will address these challenges through a combination of experimental materials science, advanced characterisation and AI‑assisted modelling.

Working within the Composites Research Group, you will develop a digital twin of the PFA cure process, combining mechanistic modelling with neural‑network‑based prediction of complex behaviours such as void formation and brittleness. In parallel, you will explore formulation and processing strategies to improve toughness, reduce embodied energy and eliminate the need for cold‑storage. The project includes four integrated work packages spanning moisture‑management strategies, toughening mechanisms, resin ageing and tack behaviour, and AI‑driven cure‑kinetics optimisation.

You will have access to world‑class facilities, including advanced imaging at the Nanoscale and Microscale Research Centre, bespoke tack‑testing equipment, and state‑of‑the‑art composite manufacturing laboratories. A three‑month placement at SHD Composite Materials will provide hands‑on experience with industrial prepreg production, specialist polymer characterisation equipment and direct involvement in manufacturing trials. 

The successful applicant will have a strong background in engineering, materials science, chemistry or a related discipline, with enthusiasm for experimental research and computational modelling. Excellent communication skills and the ability to work collaboratively with academic and industrial partners are essential.

This studentship offers an enhanced stipend of £26,780 per year for Home students, plus full tuition fees and additional support for placement travel. Applications from exceptional International students with strong research track records are welcome, but funding restrictions apply. This opportunity provides an exceptional platform for a career in advanced composites.

Please send your CV and supporting statement to: lee.harper@nottingham.ac.uk 

Closing Date: 08 May 2026
Category: Studentships

This exciting opportunity is based within the EPSRC's Centre for Doctoral Training in DigitalMetal in the Faculty of Engineering, which conducts cutting edge research into cutting-edge technologies and AI to revolutionise metals manufacturing.

Vision

We are seeking a PhD student who is motivated and capable of driving a largely experimental project to develop new techniques and knowledge. This project involves the development of a novel torsion test method to measure how oxidation and creep may interact at high temperatures / or long times, thereby aiding the safe design, operation and lifing of plant designed for long-term high-temperature service in oxidizing conditions. Moreover, the method will be used to characterise the beneficial effects of coatings aimed at increasing component lifetimes; and in future could be developed to study the effect of more damaging surface phenomena.

Motivation 

The maximum temperature that metallic materials may be used in power generation is generally determined by their creep strength. That strength is determined by creep tests on round-section test pieces.  It is also well accepted that materials subject to air, steam, combustion products etc. will also suffer from oxidation and corrosion damage, which in many cases causes metal wastage and hence increased stresses, leading to faster creep rates / shorter lives.  Oxidation forms fastest on newly created fresh surfaces, for example as the specimen tapers and begins to neck. Several other surface / environment interactions may also reduce lifetimes, including decarburisation, ingress of hydrogen, erosion by debris containing liquid metals; and susceptibility to oxidation at grain boundaries piercing the surface.

It is not surprising to consider oxidation and creep working in synergy. This is especially true when the creep strain is sufficient to cause the oxide to crack (allowing rapid supply of oxygen to the metal surface), or if the oxide spalls off altogether. Generally, creep samples with round sections will have longer lives than those with the same cross-sectional area, but in strip form or hollow tube. It is understood that specimens having higher surface area to volume ratios demonstrate that metal wastage by oxidation will reduce creep lives. The same is likely to be true for small specimen test techniques.

Power generation has always required long-term life of key components including tubes and pipework containing steam that is expanded in the steam turbine coupled to a generator to generate electricity. Typical lifetimes of 200kh are declared by the plant manufacturer. More recently the requirement of 500kh lifetimes has been mooted for the new generation of nuclear plant essential to combat climate change. Reliable declarations of such lifetimes can only be made if the combined effects of surface / environment interactions are understood and calculable. Such calculations are necessary not only to describe the increase in creep rate by air or steam oxidation, but also for, for example, the similar damaging effect of reactor coolants on fuel cladding.

Aim

At present there is no standard test method to understand the synergy between oxidation and creep. This is because several other damaging mechanisms: dislocation cell size increase, particle coarsening, embrittling precipitates or the formation of voids at grain boundaries, and other phenomena, may also cause an increase in creep rate over the long duration of a creep test. In standard creep tests a constant uniaxial tensile load is applied to a round section sample which results in the sample thinning as it is extended, and which could be due to any one or more of the mechanisms mentioned above, as well as due to oxidation. That complicates the interpretation of data. What is needed, therefore, is a test method in which creep strain is developed without changing the cross-sectional area.

This PhD proposal seeks to concentrate on the formation of oxide scale and the behaviour of coatings and their consequence on creep properties. It will develop methods in which creep strain is applied without local thinning caused by creep and instead seeks to characterise the behaviour of the oxide layer and any coating. It will seek to provide as much information as possible on these phenomena using a test piece with multiple gauge-length sections, with different cross-sectional areas and hence stress.

Candidate requirements 

This position is only open to UK students. The candidate must have at least an equivalent of a UK 2.1 class degree in materials/mechanical/ manufacturing/physics or any related discipline. This is a largely experimental research project based at the University of Nottingham, with some aspects of material modelling and development of machine learning to aid rapid modelling capabilities. We are seeking an enthusiastic, self-motivated and resourceful student to undertake this challenging project.

Essentials

• Materials/ mechanical behaviour understanding
• Engineering laboratory practical skills
• 1^st or a 2:1 class undergraduate degree in materials/mechanical/manufacturing/physics or any related discipline.

Desirables

• Basic programming skills
• Basic machine learning knowledge

Eligibility and funding 

This studentship is open to UK/home candidates. 

Funding is provided by the EPSRC's Centre for Doctoral Training in DigitalMetal and the UK High Temperature Power Plant Forum (HTPPF) and covers home tuition fees, UKRI stipend and research & training costs.

PhD start date: October 2026

Main University Supervisor: Dr Chris Hyde

Secondary University Supervisor: Prof. Tanvir Hussain

Industrial Supervisor (if applicable): Dr Chris Bullough

Programme Length: Four years

Industry Sponsor Information

The UK High Temperature Power Plant Forum (UKHTPPF) is an organisation that brings together industry, academia, and researchers to focus on the structural integrity, creep, and fatigue issues of materials used in high-temperature power plant components. Its aim is to help the power Sector to ensure the reliability and safety of high-temperature industrial materials and components.

How to apply

Application deadline: 31-May-2026

To apply, please email your CV and supporting statement to Dr Christopher Hyde at christopher.hyde@nottingham.ac.uk

Interview date: June 2026

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 May 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