School of Mathematical Sciences

Studentships and scholarships

Join an internationally-recognised centre of research excellence

PhD Scholarships available for 2023-24 

The School is able to offer a number of studentships for students, including EPSRC and BBSRC studentships funded through Doctoral Training Grants and School Scholarships.

There are up to 10 studentships available for students starting from 1 October 2023. 

School scholarships are open to both home and international students. The award provides full funding for fees (usually at UK fees rate) and living expenses.

EPSRC and BBSRC Scholarships are also available to both home and international students. These scholarships provide full funding for fees (usually at UK fees rate) and living expenses. 

The studentships are awarded on a competitive basis. All applicants who have applied and successfully completed an interview before 7 January 2023 will be considered.

Applications received after this date will be considered in subsequent rounds. Dates can be found on our admissions page. For the best chance of being awarded a scholarship we encourage you to submit your application for the January deadline.

International student scholarships in addition to School scholarships the University's International Student Recruitment team administers a number of scholarships. Many of the scholarships require an offer from the School before you can apply so early application is encouraged. 

Future School funding opportunities will be updated here throughout the year.

 Further Funding Opportunities

Biotechnology and Biological Sciences Doctoral Training Programme.  Industry-linked Studentship

This is part of the Biotechnology and Biological Sciences Doctoral Training Programme, 2023 CASE Projects

A 4-year PhD studentship beginning in October 2023.

Lead supervisor: Professor Markus Owen (Mathematical Sciences).

Project title: Machine Learning for predicting yeast phenotype from genotype for biotech applications.

The Nottingham BBSRC Doctoral Training Partnership is led by the University of Nottingham in partnership with Nottingham Trent University and the National Biofilms Innovation Centre (NBIC).  For full information on the DTP please visit our webpage:

Our CASE studentships give you the opportunity to work in partnership with industry, undertaking an industry placement and benefiting from an industrial supervisor as part of your supervisory team.  

This opportunity is open to Home students.  Funding is available for four years from 1st October 2023.  The award covers tuition fees of £4,712 at the home rate plus an annual stipend at the UKRI rate (£18,662 for 2023/24).

To check eligibility and full information on making an application please visit the BBDTP webpage:

Deadline for applications: 12.00pm (noon) Tuesday 23rd May 2023.



Stochastic analysis of biological systems

A 3.5 year PhD studentship beginning in October 2023.

Project title: Stochastic analysis of biological systems

Recent advances in technology have massively improved our ability to conduct experiments and collect data giving new insights into biological systems. The classical approach to mathematical biology has been to use analytic tools, e.g., ordinary/partial differential equations, perturbation theory etc. In this project, we will develop stochastic models and study how they are related to their deterministic counterparts. Specific examples include models from (i) epidemiology such as epidemic processes on (random) graphs, (ii) stochastic reaction networks and systems biology such as enzyme kinetic and autocatalytic reactions, (iii) ecology such as Lotka—Volterra systems. We will start with simple Markovian models and then extend them to more general non-Markovian ones. 

The goal of the project is twofold: On the theoretical side, we will investigate limiting behaviour of the stochastic systems, e.g., (functional) laws large numbers, (functional) central limit theorems, large deviations principles, stationary distributions. On the computational side, we will develop efficient simulation algorithms and statistical methodology for parameter inference.

The project will require both theoretical (probability theory, functional analysis) and computational skills (programming in Julia/R/Python/C/C++).

Specific projects in the following broad areas of mathematics:

Applied Probability

Stochastic Processes

Epidemic Modelling

Mathematical Biology

Stochastic Reaction Networks

can be found by visiting and then searching for the keywords “Wasiur” or “KhudaBukhsh”. 

Funding covers a stipend at the RCUK rate (£17,668 for 2022-23) and fees at the level of a UK domestic student. 

Enquiries for this project should be directed to Dr Wasiur Khuda Bukhsh, email:

Deadline for applications: 31st July 2023.


Faculty of Science Doctoral Training Centre in Artificial Intelligence

The Faculty of Science AI Doctoral Training Centre (DTC) invites applications from Home students for up to 6 fully-funded PhD studentships to carry out multidisciplinary research in the world-transforming field of Artificial Intelligence, commencing 1st October 2023. The studentships will have a duration of 48 months, with an annual stipend at the UKRI rate (currently £17,668).

The Faculty of Science AI DTC is a new 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 Biosciences, Chemistry, Computer Science, Mathematical Sciences, Pharmacy, Physics and Astronomy, and Psychology.

The University of Nottingham Faculty of Science AI DTC offers the opportunity to:

  • Choose from a wide choice of AI-related multidisciplinary research projects available, working with world-class academic experts in their fields;
  • Benefit from a fully-funded PhD with an attractive annual tax-free stipend;
  • Join a multidisciplinary cohort to benefit from peer-to-peer learning and transferable skills development.

For full information about the programme, the research projects available and how to apply, please visit: 

Deadline for applications: March 31st 2023


Machine learning for cardiac digital twins

A 3.5 year PhD studentship, beginning in October 2023. 

Project title: Machine learning for cardiac digital twins

Digital twins are virtual representations of physical objects, that are starting to be widely used in industry and healthcare. The twin can be used to track the health of the object, combining complex models and data. In this project, you will look at develop machine learning methodology to develop digital twins of the hearts of patients being treated for cardiac problems. In particular, looking at how to combine data that lives on manifolds, with patient specific simulations of their heart function. You will be joining a large team of researchers working on the problem, spread across Nottingham, Imperial College London, and Sheffield. As part of the project, you will be expected to collaborate with others in the team (mostly non-mathematicians), which will provide opportunities for developing a wide and valuable skillset.

Funding covers a stipend at the RCUK rate (£17,668 for 2022-23) and fees at the level of a UK domestic student.

Enquiries for this project should be directed to Dr Yordan Raykov, email:

Deadline for applications 21st March 2023.


Machine learning for first-principles calculation of physical properties

A four-year studentship, beginning in October 2023.

Project title: Machine learning for first-principles calculation of physical properties.

The physical properties of all substances are determined by the interactions between themolecules that make up the substance. The energy surface corresponding to these interactions can be calculated from first principles, in theory allowing physical properties to be derived ab-initio from a molecular simulation; that is by theory alone and without the need for any experiments. Recently we have focussed on applying these techniques to model carbon dioxide properties, such as density and phase separation, for applications in Carbon Capture and Storage. However, there is enormous potential to exploit this approach in a huge range of applications. A significant barrier is the computational cost of calculating the energy surface quickly and repeatedly, as a simulation requires. We have recently developed a machine-learning technique that, by using a small number of precomputed ab-initio calculations as training data, can efficiently calculate the entire energy surface. This project will involve extending the approach to more complicated molecules and testing its ability to predict macroscopic physical properties.

This is part of the university's Centre for Doctoral Training in Artificial Intelligence.  For further details about this centre, please visit:

Funding covers a stipend at the current UKRI rate (£17,668 for 2022-23) and fees at the level of a UK domestic student for four years.

Please contact Professor Richard Graham for further project details and to arrange an informal interview (by 21st March 2023).  Email:

Deadline for applications 31st March 2023.





School of Mathematical Sciences

The University of Nottingham
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Nottingham, NG7 2RD

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