We are looking for a
Postdoctoral Research Associate reporting to the Principal Investigator Prof Yee-Whye Teh, they will be a member of the Oxford Computational Statistics and Machine Learning (OxCSML) research group, with responsibility for managing the computational infrastructure, enabling large scale research implementation and experimentation for the project team, and collaborating on research pertinent to the project. By scaling up data, compute and model size, large language models (LLMs) have gained an impressive and ever growing array of capabilities. The next phase of development will be dominated by the use of LLMs in real world scenarios, and improving LLM reliability will be a crucial and exciting component of this next phase. The postholder will be part of a joint project across Oxford, Nanyang Technological University and National University of Singapore studying the reliability of LLMs through the lens of uncertainty quantification (UQ), Bayesian inference, conformal prediction, and world models. Along with industrial and government partners, the postholder and project team will use these methods to address current issues with LLMs, such as reward hacking, active learning and testing of LLMs. About you You will hold, or be close to completion of a PhD/DPhil in a relevant technical subject (e.g. computer science, statistics, engineering) and possess sufficient specialist knowledge, research skills and interests in deep learning and large language models to work within established research programmes and contribute ideas and solutions. You will also have the ability to independently plan and manage research projects. This post is fixed term until 14 January 2028. 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. As part of our commitment to openness, inclusivity and transparency, we would particularly welcome applications from women and black and minority ethnic candidates, who are currently under-represented in positions of this type at Oxford. Applicants will be selected for interview purely based on their ability to satisfy the selection criteria as outlined in full in the job description.