The Oxford-Man Institute of Quantitative Finance (OMI) is happy to announce fully funded Studentships in Machine Learning applied to Finance. Studentships are available for UK, EU, and International students.

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.

Applicants must have excellent written and spoken communication skills (English).Advertisements

Scholarship Description

  • Applications Deadline: January 25, 2019
  • Course Level: Studentships are available to pursue Ph.D. programme.
  • Study Subject: Studentships are awarded in Machine Learning applied to Finance.

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 modeling, 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. Dynamic learning under uncertainty for strategy and policy estimation in delayed reward environments
  5. Understanding complex dynamic relationships on graphs and networks
  6. Natural Language Processing for financial forecasting
  7. Probabilistic multi-agent models
  8. Optimization, Decision-making, and active learning
  • Scholarship Award: 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.
  • Nationality: Studentships are available for UK, EU, and International students.
  • Number of Scholarships: Three studentships are available.
  • Scholarship can be taken in the UK

Eligibility for the Scholarship

Eligible Countries: Studentships are available for UK, EU, and International students.

Entrance Requirements: Applicants must meet the following criteria:

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

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

  • A first-class honors 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).

Following skills are desirable but not essential:

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

English Language Requirements: Applicants must have excellent written and spoken communication skills (English).

Application Procedure

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 inquiries should be addressed to Prof. Steve Roberts: steve.roberts-at-oxford-man.ox.ac.uk.

Scholarship Link



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