Studentships in Machine Learning applied to Finance

Studentships in Machine Learning applied to Finance

Details of Post

The Oxford-Man Institute of Quantitative Finance (OMI) invites applications for (up to) three fully-funded studentships in the area of Machine Learning applied to Finance. Students will be supervised by members of the resident Faculty at the OMI, namely Steve Roberts, Mike Osborne, Mihaela van der Schaar, Jan-Peter Calliess, Stefan Zohren and Xioawen 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:

1. Machine learning for multi-variate time-series modelling, forecasting and event detection.

2. Information extraction and fusion from ensembles of unstructured, non-stationary data.

3. Deep (probabilistic) learning for extracting actionable insight.

4. (Deep) reinforcement learning for strategy and policy estimation in delayed reward environments.

5. Understanding complex dynamic relationships on graphs and networks.

The Oxford-Man Institute of Quantitative Finance

The Oxford-Man Institute (OMI) of Quantitative Finance is an interdisciplinary research centre in quantitative finance. It is a part of the Department of Engineering Science and has a particular 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 achieve through a unique combination of academic innovation and external engagement. The OMI has its own building in the heart of Oxford, which houses its faculty, postdoctoral researchers and students, as well as support staff. It provides excellent research facilities including outstanding computer and data resources and a well-supported seminar and conference program. The University ox Oxford and Man Group have worked in partnership since 2007 when man Group provided the cornerstone funding for the OMI, co-located with the firm's own commercial research laboratory and research staff, establishing the OMI as a world-leading interdisciplinary academic institute for research into quantitative finance. This focus will create a hub for machine learning and data analysis at Eagle House, the current home of the OMI and Man AHL's Oxford research lab. Our aim is to foster a stimulating environment composed of researchers focused on machine learning techniques, whereby machine learning and data analytics expertise can be shared and leveraged. For more information see


These studentships are funded through the Oxford-Man Institute of Quantitative Finance and are open to both UK and EU students (full award - fees plus stipend) and international students (partial award - fees at UK/EU rate paid).

Award Value

University tuition fees are covered at the level set for UK/EU students, as are Oxford College Fees (c. 7,432 in total p.a.). The stipend (tax-free maintenance grant) is c. £14,553 p.a. for the first year, and at least this amount for a further two and a half years.

Candidate Requirements

Prospective candidates will be judged according to how well they meet the following criteria:

  • A first class honours degree in Engineering, Mathematics, Statistics, Computer Sciences or Physics;
  • Experience in Machine Learning and Data Analysis;
  • Mathematical maturity with emphasis on estimation, inference and optimization theory;
  • Ability to code in high-level language, e.g. Python, R, Matlab;
  • Excellent English written and spoken communication skills.

 The following skills are desirable but not essential:

  • Experience of modelling financial - or non-stationary, heteroskedastic- data.

Application Procedure

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 18ENGIN_SRML in all correspondence to the Department and in your graduate application.

Informal enquiries should be addressed to Professor Steve Roberts (

Friday, January 19, 2018 - 12:00