Postdoctoral Research Assistant in Quantitative Finance

 

 

This post is fixed-term until Fixed-term for up to 3 years

 

The post-holders will engage in advanced study and academic research on large language models, artificial intelligence, and their applications to economic and financial problems. The successful candidate will contribute to the full range of research and academic life within the Oxford-Man Institute (OMI).

 

Reporting to the Director of the OMI, the post-holder will help ensure a healthy and vibrant research environment within the Institute. This will involve leading, devising, coordinating, and contributing to research projects involving LLMs and related AI systems, including collaborations with project partners, guidance to students, participation in and organisation of seminars and conferences, and development of further research funding. If appropriate, the role may involve acting as a formal co-supervisor for research students.

 

The post holders will hold a relevant PhD/DPhil, or be near completion, in Statistics, Computer Science, Machine Learning, Artificial Intelligence, Engineering, Mathematics, Operations Research, Economics, Finance, or a closely related subject. Preference will be given to candidates with strong expertise in large language models, foundation models, or modern AI systems and they will possess sufficient specialist knowledge of LLMs, foundation models, deep learning, NLP, mechanistic interpretability, model evaluation, or related AI methods to work within established and emerging research programmes. Research expertise in mechanistic interpretability of LLMs or foundation models is a 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. 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. You will be required to upload a statement setting out how you meet the selection criteria, a curriculum vitae, further details/other documents e.g. publications list 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 hr@stats.ox.ac.uk directly by the closing date quoting the the vacancy reference 188853.

 

Please direct informal enquiries about the post to hr@stats.ox.ac.uk, quoting vacancy reference 188853

 

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