Research Assistant in Deep Learning Theory (Fixed Term)
Fixed-term: The funds for this post are available until 30 September 2026.
We seek to appoint an researcher to contribute to our lab's research spanning areas of the theory of deep learning, robust machine learning, probabilistic deep learning and adjacent areas. This position will contribute to the research programme "Advancing Modern Data-Driven Robust AI", which is funded by UKRI through a Turing AI World-Leading Fellowship led by co-investigators Professor Zoubin Ghahramani (Department of Engineering) and Dr Ferenc Huszár (Department of Computer Science and Technology).
The programme's goal is to understand and improve modern machine learning methods primarily by casting them in a probabilistic, information theoretic, causal inference framework. More specifically, the programme is focussed on four areas: (1) Robustness; (2) Integrating symbolic and statistical frameworks; (3) Scalable probabilistic inference methods and (4) A Theory of Generalisation and Transfer Learning.
This Research Assistant will be based at the Department of Computer Science and Technology (affectionately known as the Computer Lab) and will work primarily with Dr Ferenc Huszár (Computer Laboratory) as well as other members of the Machine Learning Groups at the Computer Science and Engineering Departments.
The core responsibilities include conducting research in alignment with one or multiple components of the research program, ideally focussing on the theory of generalisation and transfer in deep learning. This includes planning and running experiments, orchestrating computational resources to enable such experiments, analyzing results, contributing to the writing of papers and reports. Contributions to teaching in these areas is not a requirement but is encouraged and welcome.
Team and Environment
This project spans two departments. This position will be based in the Computer Lab and will be embedded in the ML@CL (Machine Learning at the Computer Lab) group which includes Professor Neil Lawrence, Dr Carl Henrik Ek, Dr Challenger Mishra and Dr Ferenc Huszár as well as several other research fellows and students. The RA will have opportunities to collaborate with the Machine Learning Group at the Engineering Department to work alongside Professors Richard Turner, Carl Edward Rasmussen, Zoubin Ghahramani, David Krueger and Miguel Hernández-Lobato as well as several research fellows and students.
Our group values an open and inclusive culture. Members of the research group will be encouraged to engage in activities aimed at widening participation in Machine Learning Research, for example by contributing to summer schools, mentoring applicants and students from a variety of backgrounds.
A strong degree in computer science/mathematics/engineering or similar.
Strong programming experience in Python and knowledge of machine learning libraries/frameworks such as NumPy, PyTorch, JAX or Tensorflow.
Prior experience in carrying out machine learning research or completing relevant projects or coursework during formal education.
Solid background in branches of mathematics relevant to deep learning theory, such as probability, information theory, optimisation, linear algebra and geometry.
Publication track record in the theory of deep learning or other areas of machine learning is not expected but will be considered an advantage.
Past relevant experience as a software engineer or data scientist in an industry context, and demonstrably good software engineering practices and experience producing high-quality, reproducible code including unit tests, documentation will be considered an advantage.
Evidence of teaching or mentoring, volunteering, community building will be considered an advantage.
Click the 'Apply' button below to register an account with our recruitment system (if you have not already) and apply online.
The funds are available from June 2023.
Please note that applicants will be reviewed on a regular basis and may be invited to visit and/or interview prior to the closing date. We reserve the right to close the position early if all available positions are filled.
For informal enquiries, please contact Dr Ferenc Huszár: firstname.lastname@example.org.
You will need to upload a full curriculum vitae (CV) and a 1-page covering letter outlining your relevant past experience, and include the contact details for 2 referees. If you upload any additional documents which have not been requested, we will not be able to consider these as part of your application.
Please quote reference NR36673 on your application and in any correspondence about this vacancy.
The University actively supports equality, diversity and inclusion and encourages applications from all sections of society.
The University has a responsibility to ensure that all employees are eligible to live and work in the UK.Apply online