The Oxford-Man Institute (OMI) of Quantitative Finance is an interdisciplinary research center in quantitative finance. It is part of the Department of Engineering Science (Information Engineering) and has a focus on alternative investments and data-driven science, especially machine learning. OMI members carry out academically outstanding research that addresses the key problems facing the financial industry. Researchers create new tools and methods that can give deeper insight into financial markets – how they behave, how they become stable or unstable, how to extract value from data at scales beyond human and how they could be made to work better. This is achieved through a unique combination of academic innovation and external engagement.
Field of Study:
The Oxford-Man Institute of Quantitative Finance (OMI) invites applications for (up to) three fully-funded studentships in Machine Learning applied to Finance. Students will be supervised by members of the academic faculty at the OMI, namely Steve Roberts, Mike Osborne, Jan-Peter Calliess, Stefan Zohren and Xiaowen Dong.
Although the exact research topic is defined through discussion between student and supervisor(s), it is likely to be in one of the following broad areas:
- Machine learning for multi-variate time-series modelling, forecasting and event detection
- Information extraction and fusion from ensembles of unstructured, non-stationary data
- Deep (probabilistic) learning for extracting actionable insight
- Dynamic learning under uncertainty for strategy and policy estimation in delayed reward environments
- Understanding complex dynamic relationships on graphs and networks
- Natural Language Processing for financial forecasting
- Probabilistic multi-agent models
- Optimisation, Decision-making and active learning
University tuition fees are covered at the level set for UK/EU students, as are Oxford Course Fees (c. £7,730 in total p.a.). The stipend (tax-free maintenance grant) is c. £15,000 p.a. for the first year, and at least this amount for a further two and a half years.
- Prospective candidates will be judged according to how well they meet the following criteria:
- A first-class honours degree in Engineering, Mathematics, Statistics, Computer Science, Physics or similar;
- Experience in machine learning and data analysis;
- Mathematical maturity with emphasis on estimation, inference and optimization theory;
- Ability to code in high-level scientific development language, e.g. Python, R, Matlab;
- Excellent written and spoken communication skills (English).
The Following skills are desirable but not essential:
- Experience of modelling financial – or non-stationary, heteroskedastic – data.
How To Apply:
Candidates must submit a graduate application form and are expected to meet the graduate admissions criteria. Details are available on the course page of the University website.
Please quote 19ENGIN_SROMI in all correspondence and in your graduate application. Informal enquiries should be addressed to Prof. Steve Roberts: firstname.lastname@example.org.
25 January 2019