Based in the School of Physics Reception Office, this varied role combines front-of-house reception duties with administrative support for staff and students across both Schools. As a key point of contact, you will provide a professional and welcoming service while helping to ensure the smooth day-to-day operation of the Schools.

Key responsibilities include reception desk cover, responding to enquiries, arranging room and travel bookings, taking meeting minutes, supporting events, and managing building access requirements, including entry cards and key loan processes.

The role also has a significant procurement focus. As the administration team’s lead contact for purchasing-related queries, you will provide advice and guidance on procurement policies and procedures and work extensively with the University’s Unicore purchasing system. Responsibilities include processing purchase orders and expense claims, setting up new suppliers, resolving invoicing and payment issues, arranging financial journals between purchasing codes, and supporting financial year-end housekeeping activities.

We are seeking an individual with excellent organisational skills, strong attention to detail, and the ability to manage competing priorities effectively. The successful candidate will be confident working with financial and procurement processes, investigating issues, and identifying practical solutions.

Strong customer service and interpersonal skills are essential, as the role involves regular interaction with students, academic colleagues, professional services staff, suppliers and visitors. You will be approachable, professional and able to build positive working relationships with a wide range of people.

This is an excellent opportunity for someone who enjoys a varied administrative role, takes pride in delivering high-quality support, and can work both independently and as part of a collaborative team.

This role is Fixed Term until 31/07/2027, 36.25 hours per week, Monday to Friday, with the option to work from home at least one day per week. Additional days will be dependant on reception cover.

This role is eligible for a holiday entitlement of 27 days leave, plus standard bank holidays and five university closure days including closure between Christmas and New Year.

You will have access to a range of benefits and rewards, including fitness and health facilities, staff discounts, travel schemes and many more. To find out more about what we can offer you, follow the link to our benefits website.

For informal enquiries, please contact Rebecca.Craig@nottingham.ac.uk. Please note that applications submitted directly to this email address will not be accepted.

Closing Date: 16 Aug 2026
Category: Administrative, Professional and Managerial (APM)

The role holder will be an integral part of an interdisciplinary team of researchers covering a broad range of topics including statistical physics, machine learning and biomaterials, and based in the Condensed Matter Theory group of the School of Physics and Astronomy. We seek a motivated, skilled and independent researcher to complement our team.

The candidate will carry out the following duties:

• Plan and conduct research using recognized approaches, methodologies and techniques. 
• Manage own research activity and resolve problems, if required, in meeting research objectives and deadlines.
• Write up research work for publication.
• Contribute to the dissemination of research results at conferences.
• Build relationships with both internal and external contacts in order to exchange information, to form relationships for future collaborations, and to identify potential opportunities for collaboration.
• Collaborate with academic colleagues on areas of shared interest.
• Provide support, guidance and supervision to other members of the research group, where appropriate, in own areas of expertise.

We are looking for a motivated, skilled and highly independent researcher who can evidence:

• A minimum of an MSc in machine learning or equivalent in relevant subject area.

A strong track record in:

• machine learning and machine learning engineering
• software engineering and development

Proven Experience in:

• research in Large Language Models
• rare event analysis
• Excellent oral and written communication skills, including the ability to communicate with clarity on complex information.
• The ability to build relationships and collaborate with others.
• The ability to creatively apply relevant research approaches, models, techniques and methods.

This full-time position (36.25 hours per week) is available for the earliest start of 01/09/26 for a duration of one year.

Informal enquiries may be addressed to Prof. Juan P. Garrahan (juan.garrahan@nottingham.ac.uk). Please note that applications sent directly to this email address will not be accepted.

Closing Date: 15 Aug 2026
Category: Research and Teaching (R&T)