Postdoctoral Research Assistant in Machine Learning

We invite applications for a full-time Postdoctoral Research Associate to join the new Data-Driven Algorithms for Data Acquisition (DataAcq) project. This is a timely project developing new methodology, theory, and applications across the areas of Bayesian experimental design, active learning, probabilistic deep learning, and related topics. The £1.23M project is funded by the UKRI Horizon Guarantee for an ERC Starting Grant awarded to Prof Tom Rainforth.

The post holder will undertake innovative research as part of the RainML Lab (https://www.rainml.uk/) towards the goals of the DataAcq project. In particular, the post holder will be expected to undertake research related to one or more of the three work packages of the project: a) scalable and robust Bayesian experimental design, b) information-theoretic active learning, and c) capturing uncertainty in deep learning models (including large language models).

The successful postholder will hold or be close to the completion of a PhD/DPhil in Machine Learning, Statistics, Computer Science or closely related discipline. They will demonstrate an ability to publish, including the ability to produce high-quality academic writing. They will have the ability to contribute ideas for new research projects and research income generation. Previous research experience in one or more areas relevant to the research programme. For example: probabilistic machine learning, deep learning, experimental design, active learning, generative modelling, computational statistics, reinforcement learning, or Bayesian optimisation. This must include the ability to develop and/or analyse new methodology. Proficiency in the use of PyTorch, Tensorflow, Jax, or an equivalent deep learning library is desirable.

We proudly hold a Race Equality Charter Bronze Award and a departmental Athena SWAN Silver Award, which guide our progress towards advancing racial and gender equality. Applicants will be selected for interview purely based on their ability to satisfy the selection criteria as outlined in full in the job description. You will be required to upload a statement setting out how you meet the selection criteria, a curriculum vitae, and the contact details of two referees as part of your online application. Please note that applicants are responsible for contacting their referees and making sure that their letters are sent to [email protected] directly by the closing date.