Northeastern University London Fully Funded PhD Scholarship in Computer Science - Reliable Quantum Statistics

Doctorat / PhD Type de contrat : Full Time,Fixed-Term/Contract Date limite de candidature : 2027-09-17T00:00:00+00:00

Northeastern University London Fully Funded PhD Scholarship in Computer Science Reliable Quantum Statistics Northeastern University London > Computing, Mathematics, Engineering & Natural Sciences Funded PhD Project (UK or International Students) Funding provider: Northeastern University London (NU London) Subject areas: Project start date: October 2026 Supervisors (*lead): Osvaldo Simeone (Northeastern University London) Carlos Perez Delgado (University of Kent) Aligned programme of study: PhD in Computer Science Mode of study: Full-time Northeastern University London As part of a major investment, Northeastern University London (NU London) has multiple, fully-funded PhD studentships available to accelerate its interdisciplinary research in the humanities, social sciences and digital sciences. Each scholarship is fully-funded for three and a half years (UKRI rates) and includes fees, an annual stipend, 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 reliable, adaptive, and universal statistical methods a broad range of statistical inference tasks in quantum systems with applications to quantum sensing, computing and communications. Topics will include: Sequential and universal inference: adaptive measurement and stopping rules controlling type-I error with finite-sample guarantees. Quantum statistical modeling: use of quantum relative entropy, Fisher information, and variational quantum circuits for practical implementations. Causal and information-theoretic analysis: exploring relationships among quantum divergences and their role in statistical efficiency. Simulation and numerical validation: using small- to mid-scale quantum simulators and classical emulators to verify theoretical results. Applications: quantum sensing, computing, and communications. The successful candidates will: Have a proven, strong educational background in history or a related subject (see eligibility…