Research Assistant in Machine Learning and Quantitative Finance

Oxford-Man Institute of Quantitative Finance (OMI), Eagle House, Walton Well Road, Oxford

Research Assistant in Machine Learning and Quantitative Finance This post is fixed-term until for 1 year. Reporting to Professor Álvaro Cartea, the post holder will undertake research at the intersection of machine learning and quantitative finance, working as part of a research team led by Professor Álvaro Cartea and Professor Mihai Cucuringu. The research will focus on the development and application of modern machine learning and statistical methods to problems arising in financial markets, with particular emphasis on large-scale financial datasets and quantitative modelling. The post holder will contribute to the development, implementation and empirical evaluation of new research methodologies, and to the preparation of research outputs for publication and presentation. You will Hold, or be close to completing, a first degree or integrated Master’s degree in mathematics, statistics, computer science, engineering, or another relevant quantitative discipline, together with relevant research experience. You will also possess sufficient specialist knowledge in the discipline to work within established research programmes. 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 directly by the closing date. Please direct informal enquiries about the post to HR , quoting vacancy reference 188958 Only applications received before 12.00 noon UK time on 14.10.2026 can be considered. Interviews are anticipated to be held shortly after.