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Department of Computer Science


Probabilistic modelling and Bayesian machine learning (2025 entry onward)

Project description

I am looking for a doctoral student to join my new Manchester group to work on new probabilistic models and inference techniques. I have exciting research topics available around the following areas, and I am also open to new suggestions: (1) simulator-based inference, for combining first-principles models with learning from data, (2) Bayesian deep learning, (3) Bayesian reinforcement learning and inverse reinforcement learning, (4) with multi-agent modelling, and (5) privacy-preserving machine learning and synthetic data generation. Can be theoretical or applied work or both; the group has excellent opportunities for collaboration with top-notch partners in multiple applications, including user interaction, health, medicine, synthetic biology and digital twins.

Person specification

For information

Essential

Applicants will be required to evidence the following skills and qualifications.

  • You must be capable of performing at a very high level.
  • You must have a self-driven interest in uncovering and solving unknown problems and be able to work hard and creatively without constant supervision.

Desirable

Applicants will be required to evidence the following skills and qualifications.

  • You will have good time management.
  • You will possess determination (which is often more important than qualifications) although you'll need a good amount of both.

General

Applicants will be required to address the following.

  • Comment on your transcript/predicted degree marks, outlining both strong and weak points.
  • Discuss your final year Undergraduate project work - and if appropriate your MSc project work.
  • How well does your previous study prepare you for undertaking Postgraduate Research?
  • Why do you believe you are suitable for doing Postgraduate Research?