Fixed-term: The funds for this post are available until 30 September 2026. Role Summary We seek to appoint an independent researcher to develop and drive a research program at the intersection of the fields of kernel methods, hypothesis testing, robustness and privacy. 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 Prof Zoubin Ghahramani (Department of Engineering) and Dr Ferenc Huszár (Department of Computer Science and Technology). The research program will be conducted in collaboration with Prof Arthur Gretton from the Gatsby Computational Neuroscience Unit. 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. For this position, preference will be to select applicants with expertise on kernel hypothesis testing, ideally with a focus on robustness and privacy. This RA/SRA will be jointly based at the Department of Computer Science and Technology at Cambridge, and at the Gatsby Computational Neuroscience Unit in London. The RA/SRA will work primarily with Dr Ferenc Huszár (Computer Laboratory) in collaboration with Prof Zoubin Ghahramani (Engineering Department), and with Prof Arthur Gretton at the Gatsby Unit. The core responsibilities include planning and conducting research in alignment with one or multiple components of the research program, ideally focussing on developing machine learning methods with kernel hypothesis testing, robustness, privacy and related topics. Additional responsibilities include developing research objectives and proposals; presentations and publications; assisting with the supervision of research students; liaising and networking with colleagues and students; planning and organising research resources and workshops. Team and Environment This position will involve collaboration across two universities: the University of Cambridge, and University College London. The SRA will be based with the MLCL (Machine Learning at the Computer Lab) group which includes Prof Neil Lawrence, Dr Carl Henrik and Dr Ferenc Huszár as well as several other research fellows and students. The research program will be co-supervised at the Gatsby Computational Neuroscience Unit, with faculty comprising Prof Maneesh Sahani, Prof Peter Latham, Prof Peter Orbanz, Dr Andrew Saxe, Dr Agostina Palmigiano, Dr Leena Chennuru Vankadara and Prof Arthur Gretton. It is expected that the SRA will spend a significant amount of time at the Gatsby Unit, to enable research collaboration. The SRA will further collaborate with the Machine Learning Group 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. MLCL and the Gatsby Unit value 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. Click the 'Apply' button below to register an account with our recruitment system (if you have not already) and apply online. Expected Qualifications PhD 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 and publishing machine learning research (typically at NeurIPS, ICML, ICLR, AISTATS, ALT, COLT, JMLR or similar) related to one of the focus areas mentioned above, preferably in kernel hypothesis testing. Strong background in branches of mathematics relevant to machine learning and kernel methods, such as statistics, probability, information theory, optimisation and linear algebra. Beneficial Qualifications Research experience and exceptional publication track record (e.g., best-paper award, oral, spotlight, etc.) in kernel methods, hypothesis testing, robustness or privacy. Prior experience with relevant large scale and rigorous machine learning experiments. Good software engineering practices and experience producing high-quality, reproducible research code including unit tests, documentation. Experience mentoring more junior colleagues. For informal enquiries, please contact Dr Ferenc Huszár: fh277cam.ac.uk. You will need to upload a full curriculum vitae (CV) and a 2-page research proposal, and to include the contact details for 3 referees. The research proposal should briefly cover relevant past experience and should propose at least one new project in depth fitting the theme of this position. If you upload any additional documents which have not been requested, we will not be able to consider these as part of your application. The University has a responsibility to ensure that all employees are eligible to live and work in the UK. Please note that we provide the support of applying for the relevant visa (if required) and we reimburse the cost of the first visa. Please quote reference NR45405 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. Further information Further information Apply online