3 fully-funded Studentships in Machine Learning 1540 views

The Oxford-Man Institute of Quantitative Finance (OMI) at University of Oxford invites applications for (up to) three fully-funded studentships in Machine Learning applied to Finance. The selected 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.

Research 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

Award Value: 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.

Start date: October 2019

Eligibility

• Applications are open to both UK & EU students (full award – fees plus stipend) and international students (partial award – fees at UK/EU rate plus stipend).
• Applicant must hold 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).

Application:

• Graduate application form with quoted reference number 19ENGIN_SROMI.
• Informal enquiries should be addressed to Prof. Steve Roberts: steve.roberts@oxford-man.ox.ac.uk.