University of Nottingham, England invites online Application for number of Fully Funded PhD Degree at various Departments. We are providing a list of Fully Funded PhD Programs available at University of Nottingham, England.
Eligible candidate may Apply as soon as possible.
(01) PhD Degree – Fully Funded
PhD position summary/title: PhD Studentship: Computational Chemistry
The selected PhD candidate will work with Prof Elena Besley on computational modelling of next-generation semiconductors made from atomically thin materials in a groundbreaking £6 million EPSRC Programme Grant to reduce the soaring energy demands of artificial intelligence. The computational team will work in collaboration with many partners in academia and industry to address challenges in the science and technology of atomically thin semiconductor for low-energy-consumption electronics.
Deadline : 21 April 2026
(02) PhD Degree – Fully Funded
PhD position summary/title: PhD Studentship: AI Doctoral Training Centre (DTC)
The Faculty of Science AI Doctoral Training Centre (DTC) invites applications from Home students for fully-funded PhD studentships to carry out multidisciplinary research in the world-transforming field of Artificial Intelligence, commencing 1st October 2026. The studentships will have a duration of 42 months, with an annual stipend at the UKRI rate (currently £21,805).
The Faculty of Science AI DTC is an initiative by the University of Nottingham to train future researchers and leaders to address the most pressing challenges of the 21st Century through foundational and applied AI research on a cohort basis. The training and supervision will be delivered by a team of outstanding scholars from different disciplines cutting across Arts, Engineering, Medicine and Health Sciences, Science and Social Sciences.
Deadline : 19 April 2026
View All Fully Funded PhD Positions Click Here
(03) PhD Degree – Fully Funded
PhD position summary/title: PhD Studentship: Rolls-Royce and EPSRC funded PhD – Experimental and numerical studies into the wear of articulating spline couplings for aeroengine applications
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.
Deadline : 17 June 2026
(04) PhD Degree – Fully Funded
PhD position summary/title: PhD studentship: Breaking Design Silos with AI: A Knowledge-Centric Framework for Integrated Aerostructure Design
We are seeking a PhD student that is motivated in system engineering, structural, aerodynamic & manufacturing process modelling and optimisation techniques that will transform current design & development practices. Together we will make technological advances towards the next-generation co-design platform, accelerating the development cycle for complex interdisciplinary systems.
Deadline : 02 May 2026
(05) PhD Degree – Fully Funded
PhD position summary/title: PhD Studentship: Smart Composite Structures with Integrated Optical Fibre Sensing
We are looking for an outstanding PhD student with either strong background in computational modelling or significant experience of laboratory work, who is keen to work at the interface between simulation, composites manufacturing and advanced sensing techniques. The project will provide opportunities to develop skills in these areas and contribute to the development of the next generation composite structures.
The project brings together two research groups to create the next generation of composite structures. These structures will combine high-performance and integrated sensing starting from the manufacturing process. The embedded optical fibre sensing will be used in conjunction with advanced numerical models for monitoring of composites manufacturing and structural performance.
Deadline : 02 May 2026
Polite Follow-Up Email to Professor : When and How You should Write
Click here to know “How to write a Postdoc Job Application or Email”
(06) PhD Degree – Fully Funded
PhD position summary/title: PhD studentship: AI-enhanced modelling of liquid hydrogen flows for net-zero transportation
This exciting opportunity is based within the Mechanical and Aerospace Systems Research Group at Faculty of Engineering which conducts cutting edge research into thermofluids in applied fields such as fuel systems, transportation and power generation.
We are seeking a highly motivated PhD researcher with a passion for fluid dynamics, AI, and sustainable aviation. The vision of this PhD is to create the next generation of modelling tools for liquid hydrogen (LH₂) fuel systems—a critical requirement for future hydrogen-powered aircraft concepts. This opportunity will drive advances in cryogenic modelling, two-phase CFD, and AI-based reduced-order models to accelerate modelling capability in net‑zero aerospace technologies.
Deadline : 01 May 2026
(07) PhD Degree – Fully Funded
PhD position summary/title: PhD Studentship: Aircraft Electrical Power System Stability
This exciting opportunity is based within the Power Electronics and Machines Centre (PEMC) Research Group at Faculty of Engineering which conducts cutting edge research into enabling technologies for future aircraft applications.
We are seeking PhD student that is motivated to conduct research in electrical power system stability for future aircraft applications. Together we will make technological advances that will lead to more sustainable and safe air travel.
Deadline : 01 May 2026
(08) PhD Degree – Fully Funded
PhD position summary/title: PhD Studentship: Pioneering Multilayer Nitride Dielectrics: A New Materials Architecture for Ultra-High-Voltage Electronics
We are seeking a highly motivated and ambitious PhD researcher who is excited by fundamental materials science and its application to real-world technologies. This project aims to redefine how dielectric failure is understood and controlled, introducing a new architecture-led design approach rather than relying on incremental optimisation of existing materials.
By developing novel multilayer dielectric materials with ultra-high breakdown strength, the research will revolutionise electrified technologies, enabling operation at substantially higher power densities, voltages and temperatures. This capability will unlock more compact, efficient and robust electronic and power systems, directly supporting future electrification and Net Zero ambitions.
Deadline : 01 May 2026
Click here to know “How to Write an Effective Cover Letter”
(09) PhD Degree – Fully Funded
PhD position summary/title: PhD studentship: Powering the Future of Sustainable Grids Through Multiport Home Energy Management
This exciting opportunity is based within the Power Electronics and Machines Control Research Institute at Faculty of Engineering which conducts cutting edge research into power electronics for energy management and decarbonization.
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 into our homes.
Deadline : 01 May 2026
(10) PhD Degree – Fully Funded
PhD position summary/title: PhD Studentship: Sustainable road binders derived from a biogenic supply chain
This PhD offers an exciting opportunity to develop sustainable, biogenic materials for use as road construction binders. Current road infrastructure depends heavily on petroleum‑derived bitumen and non‑renewable aggregates, contributing significantly to carbon emissions, land use, and resource depletion.
Biobased binders are emerging as promising alternatives, offering potential benefits in carbon reduction, durability, and ageing resistance. However, widespread adoption is limited by inconsistent performance data, insufficient understanding of long‑term behaviour, and a lack of standardised testing.
This project will investigate new technological pathways for producing renewable, biogenic road binders that, when used in asphalt mixtures, could transform pavements into long‑term carbon sinks. The research will assess mechanical performance, durability, carbon reduction potential, economic feasibility, and alignment with circular‑economy principles.
Deadline : 30 April 2026
Connect with Us for Latest Job updates
(11) PhD Degree – Fully Funded
PhD position summary/title: PhD Studentship: Machine Learning – Enhanced Boundary Layer Modelling for Industrial CFD in partnership with Siemens Digital Industry Software
The project focuses on developing and integrating ML techniques to enhance wall treatments for under-resolved boundary layers in aerodynamic simulations for industrial applications. In many industrial settings, complex geometries and restricted computational resources make it impractical to generate sufficiently refined near-wall meshes, limiting the accuracy of conventional boundary layer modelling approaches.
During the PhD, the student will curate an archive of high-fidelity simulation data spanning a range of representative application areas, which will be used to train and assess boundary layer neural network models. The student will develop and evaluate suitable ML architectures, analysing the trade-offs between different modelling strategies and levels of fidelity. By the end of the project, the student will demonstrate the integration of ML-based boundary layer models within an open-source finite volume CFD code and quantify their performance relative to current pragmatic industrial approaches.
The successful candidate will spend at least 3 months during the PhD based within Siemens Digital Industry Software, receiving joint supervision and training from both academic and industrial researchers, and gaining direct exposure to industrial CFD workflows and software development practices.
Deadline : 29 April 2026
Polite Follow-Up Email to Professor : When and How You should Write
(12) PhD Degree – Fully Funded
PhD position summary/title: PhD Studentship: Turbulence detection in blood flow using 4D MRI
Flow disturbances in blood flow are vital sign of cardiovascular diseases, suggesting a development of turbulent flow due to abnormal heart movement or blocking of the arteries. In recent years, time-resolved Magnetic Resonance Imaging (4D MRI) technique has been developed to detect flow turbulence where the Doppler ultrasound technique does not give reliable diagnosis due to the complexity of the diseases. Since the 4D MRI allows the analysis of complex and unsteady flow patterns deep in the human body, it is suitable for the visualisation and analysis of valvular heart disease or atherosclerosis. However, the clinical decision-making in the use of 4D MRI is restricted only to special cases due partly to the long scanning time required, and partly to the inaccuracy of turbulence measurements. These are the main issues that the proposed PhD study will address. The research work will be conducted by using a vascular flow phantom, guiding the MRI scanning strategy to improve the turbulence detection and quantification. The flow turbulence and velocity in a vascular flow phantom will be measured by Particle Image Velocimetry (PIV), against which MRI data will be compared and calibrated. In-silico technique based on Computational Fluid Dynamics (CFD) will also be developed to provide further information necessary for the development of new MRI image scanning strategies.
Deadline : 28 April 2026
(13) PhD Degree – Fully Funded
PhD position summary/title: PhD studentship: Digital-Twin Technology to Accelerate Development of Electric Propulsion Systems
This exciting opportunity is based within the Power Electronics, Machine and Control Research Institute at Faculty of Engineering which conducts cutting edge research using the Digital Twin Technology to accelerate electric propulsion system development.
Applications are invited for the above research studentship to join the Power Electronics, Machines and Drives Research Group at the University of Nottingham. The PEMC group has undergone a significant period of growth and now has over 150 members, with 18 academics (including 7 full professors) and approximately 120 PhD students and post-doctoral research fellows. The group has excellent facilities for experimental work including approximately 2500m2 of research space and a construction and testing capability up to 5MW
Deadline : 23 April 2026
(14) PhD Degree – Fully Funded
PhD position summary/title: EPSRC PhD Studentship: Electrophysical remanufacturing of aerospace gas turbine components for performance restoration and critical material safeguarding
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.
Deadline : Open until filled
(15) PhD Degree – Fully Funded
PhD position summary/title: EPSRC PhD Studentship: Looking backwards to go forwards: Systems Engineering Approaches for Inverse Design of Manufacturing Systems
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.
Deadline : Open until filled
(16) PhD Degree – Fully Funded
PhD position summary/title: EPSRC PhD Studentship: Novel Optics and AI Aproaches to Image the Centre of a Live Root for the First Time.
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.
Deadline : Open until filled
(17) PhD Degree – Fully Funded
PhD position summary/title: EPSRC PhD Studentship: Retrofitting UK Schools for Health, Performance and Climate Resilience
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.
Deadline : Open until filled
(18) PhD Degree – Fully Funded
PhD position summary/title: EROG01 Studentship: Advanced 3D-Printing of Responsive Biomaterial Devices
Responsive 3D-printed functional devices interact with their environment, responding to stimuli (temperature, light, etc.), and “4D-printed” devices respond over time (e.g. changing shape, releasing medicines), controlled by the arrangement of the various dissimilar materials within them. The goal of this project will be to develop responsive 4D-printed biomaterial devices for advanced personalised drug delivery. The student will formulate new 3D-printable materials and develop new design methods, for functional 4D-printed devices with either fast self-resetting responses or complex multi-scale shape changes, applicable to biomedical, micromechanical, or optoelectronic applications.
Deadline : Open until filled
(19) PhD Degree – Fully Funded
PhD position summary/title: FROG03 PhD Studentship: 3D-Printed Drug Delivery “Microbots” for Personalised Healthcare
Applications are invited for a PhD project within the Faculty of Engineering, in the Centre for Additive Manufacturing research group (CfAM) at the University of Nottingham. The student will work in world-class laboratory facilities in the CfAM engaging with interdisciplinary team with expertise in 3D printing, bio-printing for medical applications, micro-robotics, and materials science.
Deadline : Open until filled
(20) PhD Degree – Fully Funded
PhD position summary/title: PhD Studentship: Preclinical modelling and therapeutic targeting of glioblastoma infiltrative margin
Glioblastoma (GBM) is an incurable malignant brain tumour with severely limited therapeutic interventions and short survival times. Major challenges in treating GBM include intra-tumour heterogeneity and invasion into the adjacent healthy brain. Such invasive tumour subpopulations reflect residual disease intractable to standard multimodal treatment, and which is responsible for GBM recurrence. We have revealed distinct gene expression profiles of the infiltrative margin of glioblastoma via bulk transcriptomics https://pubmed.ncbi.nlm.nih.gov/37434262/ predicated on biopsies obtained via 5-aminolevulinic (5-ALA)-guided neurosurgery.
We now aim to resolve infiltrative margin biology at high resolution using single cell and spatial transcriptomic methods, to identify actionable therapy targets which could lead to informed delivery of personalised medicine approaches.
The appointed will work with genome, computational and cancer biologists at the University of Nottingham to develop and characterise patient-derived explant models amenable for drug repurposing studies. The project also introduces a collaboration with Queen’s Mary University, London, whereby 5ALA-negative astrocytes from the glioblastoma infiltrative margin will be re-programmed to generate induced pluripotent stem cells as a patient-matched toxicity control.
Deadline : 01 May 2026
(21) PhD Degree – Fully Funded
PhD position summary/title: PhD Studentship: School for Primary Care Research (SPCR) & ARC EM Joint PhD Studentship opportunity at the University of Nottingham
Applications for a PhD studentship are invited from individuals with a strong academic record who wish to develop a career in primary care research, with a particular focus on healthy ageing. This award is offered jointly by the NIHR School for Primary Care Research at the University of Nottingham and NIHR Applied Research Collaboration East Midlands and offers project-specific training in areas of particular importance to primary care, with details of training opportunities for SPCR Trainees found here: https://www.spcr.nihr.ac.uk/career-development/spcr-trainees.
The award includes home tuition fees and an annual tax-free stipend at UKRI rates. Students with overseas status are welcome to apply but will be required to provide written confirmation that they can fund the remainder of their fees from alternative sources at time of application. The award will be taken up on 1 October 2026.
Applicants must have a first degree in a relevant discipline e.g. public health, medical statistics, social sciences, health economics, health psychology or have a clinical background, and will be expected to complete a PhD during the award period.
Deadline : 27 April 2026
(22) PhD Degree – Fully Funded
PhD position summary/title: PhD Studentship: Spin-Polarised Scanning Probe Microscopy of Unconventional Magnets
This project will develop and apply spin-polarised scanning probe microscopy to image, understand, and control magnetic order at the atomic scale in unconventional magnetic materials. Using low-temperature scanning tunnelling microscopy (STM) and atomic force microscopy (AFM), the work will detect and map complex magnetic textures in emerging material classes, including two-dimensional magnets, altermagnets, and new classes of compensated spin-split systems. These materials exhibit magnetic order without conventional ferromagnetism, offering new routes to functional behaviour rooted in crystal symmetry, topology, and electronic structure rather than net magnetisation.
A core scientific aim is to resolve and manipulate topological magnetic textures such as vortices, merons, and domain walls at the level of individual atomic sites. Topological defects are central to modern condensed-matter physics, underpinning phenomena ranging from superconductivity to superfluidity, yet they are rarely accessible as individual objects in real materials. By combining spin-polarised STM with controlled current injection, local electric fields, and temperature modulation, this project will move beyond passive imaging to actively create, annihilate, and reconfigure magnetic textures on demand. This capability will establish direct causal links between atomic-scale structure, symmetry breaking, and emergent magnetic topology.
The project will place particular emphasis on newly discovered altermagnetic materials, which break time-reversal symmetry while remaining magnetically compensated. These systems have generated strong international interest due to their compatibility with superconductors and topological phases, and their potential for highly scalable, low-energy spintronic devices. While recent studies have demonstrated nanoscale imaging of altermagnetic vortices and domains, the microscopic mechanisms governing their stability, dynamics, and interaction with defects remain largely unexplored. Atomic-scale scanning probe measurements will directly address this gap, providing insight into the fundamental limits of altermagnetic order and its controllability.
Deadline : 23 April 2026
(23) PhD Degree – Fully Funded
PhD position summary/title: PhD Studentship: Machine Learning for Probabilistic Modelling of Non-equilibrium Time Series Beyond the Markovian Paradigm
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)
Deadline : Open until filled
(24) PhD Degree – Fully Funded
PhD position summary/title: Studentship: Developing consensus and guidance for the integration of exotic animal medicine into the UK undergraduate veterinary curriculum
This project will develop consensus-based guidelines on day one competency for exotic animal medicine using mixed-methods research approaches.
Specifically, this project aims to:
- Establish expert consensus on the key species, knowledge, and clinical skills required for new graduates to practise exotic animal medicine in first-opinion settings.
- Capture pet owner perspectives using an “Ideas, Concerns and Expectations” focus group approach.
- Develop a structured NTCA competency framework aligned with the RCVS Day One Competences and AAVMC Competency-Based Veterinary Education framework.
- Produce practical curriculum guidance to support veterinary schools in integrating exotic animal teaching within existing resource and time constraints, ensuring that graduates are competent to manage exotic animal cases in first-opinion practice.
As part of this project, you will gain mixed-methods research experience, including data collection and analysis using both quantitative and qualitative methods. Prior experience with qualitative research (eg, interviews, focus groups) is beneficial, but not essential. Training appropriate for the student will be provided.
Deadline : 20 April 2026
(25) PhD Degree – Fully Funded
PhD position summary/title: PhD Studentship: Machine Learning Density Functionals from Quantum Computing
Data is more valuable than oil, so it has been said. Quantum computing offers new unusual datasets thereby presenting new opportunities for AI approaches. Quantum computing is raising the prospect of calculations on a hardware architecture that matches the inherent nature of quantum chemistry electronic structure calculations and with it the opportunity to capture some of the inherent physics, albeit with the noise associated with near-term quantum devices. This in turn offers an exciting new dataset from which it will be possible to use machine learning to train a more accurate functional for use in density functional theory. In collaboration with Phasecraft, a leading quantum algorithms company, this project will explore the generation of new quantum computing datasets and the development of machine learning techniques to utilize the datasets to train improved density functionals for use in quantum chemical electronic structure calculations.
Deadline : 19 April 2026
(26) PhD Degree – Fully Funded
PhD position summary/title: PhD Studentship: Multidisciplinary Research in Artificial Intelligence
The Faculty of ScienceAI Doctoral Training Centre (AI DTC) invites applications from Home students for fully-funded PhD studentships to carry out multidisciplinary research in the world-transforming field of Artificial Intelligence, commencing 1st October 2026.
Each studentship is funded for 42 months and includes a UKRI-rate annual tax‑free stipend to cover living costs (currently £21,805).
The AI DTC is an initiative by the University of Nottingham to develop future researchers and leaders who can address key challenges of the 21st century through foundational and applied AI research. Training and supervision are delivered by a diverse team of researchers from across Arts, Engineering, Medicine and Health Sciences, Science and Social Sciences.
We encourage applications from individuals of all backgrounds, particularly those who may be underrepresented in AI research. The University of Nottingham is committed to improving accessibility to postgraduate research for students from widening‑participation backgrounds, and we welcome applications from candidates with non‑traditional or varied educational and professional journeys.
Deadline : 19 April 2026
About The University of Nottingham, Nottingham, England –Official Website
The University of Nottingham is a public research university in Nottingham, England. It was founded as University College Nottingham in 1881, and was granted a royal charter in 1948. The University of Nottingham belongs to the research intensive Russell Group association.
Nottingham’s main campus (University Park) with Jubilee Campus and teaching hospital (Queen’s Medical Centre) are located within the City of Nottingham, with a number of smaller campuses and sites elsewhere in Nottinghamshire and Derbyshire. Outside the UK, the university has campuses in Semenyih, Malaysia, and Ningbo, China. Nottingham is organised into five constituent faculties, within which there are more than 50 schools, departments, institutes and research centres. Nottingham has more than 46,000 students and 7,000 staff across the UK, China and Malaysia and had an income of £792.2 million in 2021–22, of which £131.4 million was from research grants and contracts. The institution’s alumni have been awarded one Nobel Prize, a Fields Medal, and a Gabor Medal and Prize. The university is a member of the Association of Commonwealth Universities, the European University Association, the Russell Group, Universitas 21, Universities UK, the Virgo Consortium, and participates in the Sutton Trust Summer School programme as a member of the Sutton 30
Disclaimer: We try to ensure that the information we post on VacancyEdu.com is accurate. However, despite our best efforts, some of the content may contain errors. You can trust us, but please conduct your own checks too.
Related Posts
