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

Doctorat / PhD Type de contrat : Part Time,Fixed-Term/Contract Date limite de candidature : 2026-11-15T00:00:00+00:00

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…