Studentships and scholarships

A three-year studentship, beginning in October 2025.

This project is an exciting opportunity to undertake industrially linked research in partnership with the Manufacturing Technology Centre (MTC). It is based within the School of Mathematical Sciences at the Faculty of Science, University of Nottingham, which amongst its wide research portfolio, conducts cutting edge research into the development novel causal inference, automation and robotic control algorithms.

We are seeking for a highly motivated PhD student to conduct cutting edge research of the AI techniques and reinforcement learning, a technology which has powered many of the recent groundbreaking self-guided game engines and large language models.

Together we will study how the existing and emerging paradigms in reinforcement learning can be utilized to power automated annotation and diagnostic software of critical infrastructure via continual learning from sensor feedback.

This PhD aims to develop novel algorithms for Artificial Intelligence (AI) driven continual learning via reinforcement learning (RL), incorporating mechanistic knowledge through causal inference constraints. These algorithms will enable adaptive digital systems for assisted and automated annotation software, as well as diagnostics software, particularly in the contexts of manufacturing and precision imaging. By leveraging causal insights, the project will enhance the systems' ability to learn dynamically from sensor feedback while maintaining consistency and reliability. In the later stages, the research will apply these advancements to a case study on adaptive disassembly lines, demonstrating how continual learning can drive more efficient and sustainable solutions in complex, evolving environments.

You will have the opportunity to join a multidisciplinary team of supervisors: experts in engineering and biochemistry related to different battery technologies; experts in foundational computer science and mathematical foundations of AI; and experts in the industrial utilisation of emerging AI technologies for various manufacturing and built environment inspection processes.

Funding: This 3-year fully funded studentship is open to UK home students. The successful applicant will receive a generous tax-free annual stipend of £25,000 plus payment of their full-time home tuition fees. Additionally, £2,000 per annum is provided for consumables, travel, etc. Due to funding restrictions this PhD position is only available to UK nationals. As this position is sponsored by the MTC, any successful candidate would need to pass the sponsor's own security checks prior to the commencement of the PhD.

For full details please see the project information page.

For informal enquiries please send a detailed CV and academic transcripts to Dr Yordan Raykov and Dr Yazan Qarout

Deadline for applications 28 February 2025

PhD Studentship in Neurocomputation in networks with plastic delays.

Applications are sought for a fully-funded 42 month PhD studentship to work with Dr Rachel Nicks and Prof Stephen Coombes on the project: White Matter Computation: Utilising axonal delays to sculpt network attractors, funded by The Leverhulme Trust.

Proposed PhD Start Date: 1st October 2025.

This is a brain inspired project in the field of Neurodynamics. Networks of oscillators are ideal candidates for modelling patterns of functional connectivity seen in large scale brain recordings. These describe correlations between brain regions and can evolve over tens of seconds, with essentially discontinuous shifts from one short term state to another that can be viewed as heteroclinic connections between phase-locked states. The PhD project will consider the role that communication delays between nodes can have in shaping patterns of dynamic functional connectivity. This will include consideration of the fact that communication delays in the brain are plastic as they are modulated by the thickness of the myelin surrounding the axonal connections which can change over time, depending on neural activity.

Incorporating the dynamics of the state-dependent delays will require the development of new tools for coupled oscillator theory in time-delayed systems of differential equations. The resulting models will be analysed with analytical tools from applied mathematics and numerical studies in the Julia programming language. The successful candidate should have a strong mathematical background, particularly in dynamical systems theory, and a keen interest in network science, and scientific computation. The student will gain invaluable experience that will serve as a springboard for further academic and professional development in this exciting, cutting-edge area of research at the intersection of applied mathematics and neuroscience.

Requirements

• The candidate should have a 1st or high 2:1 degree in applied mathematics, or a closely related subject with substantial mathematical content.
• Skills in programming (MATLAB, Julia) and numerical bifurcation theory (e.g. DDE-BIFTOOL, MATCONT) are desirable.
• Background knowledge in one or more of dynamical systems theory, bifurcation theory or delay-differential equations would be advantageous.

Interested candidates should email their CV and a personal statement covering their background, general motivation to undertake a PhD and their interest in this project to Dr Rachel Nicks. 

Interviews will take place during May 2025. Post interview, strong candidates will be encouraged to make an application through MyNottingham stating Dr Rachel Nicks and Stephen Coombes as supervisors and “White Matter Computation: Utilising axonal delays to sculpt network attractors” as the project title.

For full details please see the project information page.

For informal enquires please contact Dr Rachel Nicks

Closing date: 30 April 2025