PhD Studentship in Computer Science - Algorithms/Hardware Co-Design for Bayesian and Ising Models (Fully Funded)

About this position

Funded PhD Project (UK or International Students)

Funding provider: Northeastern University London (NU London) and IMEC

Project start date: January 2027

Supervisors (*lead):

Bipin Rajendran (Northeastern University London)

Darren Hurley-Smith (University of Kent)

Northeastern University London

As part of a major investment, Northeastern University London (NU London) offers PhD studentships to support interdisciplinary research across the humanities, social sciences and digital sciences.

The current studentship is part-funded by IMEC

and is offered for four years and includes funding towards fees, a stipend at UKRI rates, an additional London allowance, and associated costs such as training.

NU London is both a UK university governed by UK higher education regulations, and the European campus of Northeastern University – a large, top-tier research intensive, Boston-based institution. Founded in 1898, Northeastern received $230.7m of external research funding in 2022, and is the recognized leader in experience-driven lifelong learning. It has campuses across the United States and Canada (in Boston; Charlotte, North Carolina; Portland, Maine; Oakland, California; San Francisco; Seattle; Silicon Valley; Arlington, Virginia; the Massachusetts communities of Burlington and Nahant; Toronto and Vancouver).

Whilst the PhD will be a UK qualification, students will have the opportunity to engage with and visit the Northeastern University network overseas as part of their London-based doctoral studies, providing a truly unique and highly sought-after dimension to their research training.

The Project

The project aims to develop algorithms and hardware architectures for Bayesian and Ising Models that can leverage the intrinsic stochasticity of emerging magnetic random access memory (MRAM) devices.

Topics will include:

Develop robust methodologies for characterising the stochastic behaviour of existing MRAM devices.

Establish the link between device-level statistics and probabilistic algorithms.

Build theoretical grounding in probabilistic models (Bayesian and Ising) and how they can be optimized considering the natural characteristics of MRAM devices.

Use simulation and emulation tools combined with profiling frameworks for energy, latency, and throughput.

Benchmark across representative workloads (e.g., TinyML, edge vision, federated learning).

The successful candidates will:

Have a proven, strong educational background in Computer Science or a related subject (see eligibility criteria)

Be excited and inspired by the proposed project area

Be a self-starter

Have good communication skills

Have an inquiring mind and be willing to challenge themselves

The successful candidates will benefit from a brand new campus on the banks of the River Thames next to Tower Bridge. This is an interdisciplinary, vibrant research environment with international collaboration and networking opportunities and dedicated research space. It will form the hub of a highly experienced, multi-institution supervisory team from NU London and the University of Kent.

Shortlisted candidates will be interviewed in November 2026. Candidates are welcome to contact the NU London supervisor with informal enquiries before the application deadline: 15/11/2026

(b.rajendran@nulondon.ac.uk)

The panel will be shortlisting for this position on a rolling basis so please apply as soon as possible. We reserve the right to close this post before the closing date if we receive a sufficient number of applications.

Eligibility

Bachelor's degree in a relevant subject - 2:1 or 1st (essential)

Master’s degree in a relevant subject (optional)

English Language requirements:

If applicable – IELTS 6.5 overall (with a score of at least 6.5 in each individual component) or equivalent.

Nationality

Applications are open to UK and international students. Please indicate if you are likely to require a visa on your application. We are unable to support visa costs.

Funding

This scholarship covers the full cost of tuition fees, an annual stipend and an additional London allowance (set at UKRI rates) for 3.5 years.

How to Apply

Applications should be made via the

‘Apply’

button above. Please reference your application “

R143025

”.

Please upload a single PDF document comprising of (1) a CV and (2) Covering Letter stating how you meet the requirements and why you are interested in the proposed research project.

Participation in the equal opportunities section is encouraged, but voluntary.