Willowgrove Studentship in Machine Learning

Willowgrove Studentship in Machine Learning

The Machine Learning Research Group invites applications for a funded studentship in Machine Learning. The successful applicant will be supervised by Steve Roberts & Mike Osborne. Although the exact research topic is defined through discussion between student and supervisors, it is likely to be in one of the following broad areas:

  • Probabilistic numerics

  • Bayesian deep learning

  • Bayesian optimization

  • Scalable machine learning

The Machine Learning Research Group

The Machine Learning Research group unites pioneering work on foundational machine learning topics with the application of that work to applications motivated by great societal and scientific challenges.  Our work on machine intelligence promises technologies that will free the potential of human intelligence. The group develops systems that can provide decision making upon data at a scale beyond the human, while realising the benefits of subtle human judgement and creativity. Our work has found impact in vast data arenas, such as Zooniverse (where it is used to optimally combine millions of decisions). The group has also led the development of Probabilistic Numerics, an approach to machine learning that introduces intelligence to every level of an algorithmic pipeline. Specifically, this approach augments existing high-level machine learning models with intelligent numerical techniques, ensuring modularity and the correct propagation of decisions through the system.

This work has found application in domains ranging from astronomy and finance to biomedical engineering and zoology. In the former, the group’s work has been incorporated into the Kepler space telescope pipeline for the detection of planets in distant solar systems as well as forming components of the data analyses in such projects as the Square Kilometre Array; in the latter, it has led to winning high-profile funding such as the Google Impact Challenge to detect disease-bearing mosquitoes. The group has also addressed the broader societal consequences of machine learning and robotics, working to analyse how intelligent algorithms might soon substitute for human workers, and predicting the resulting impact on employment. The group has a strong focus on machine learning applied to commercial and industrial problem domains, including finance. For more information see www.robots.ox.ac.uk/~parg.

Eligibility

The studentship is open to applicants who are normally resident in the UK and is a full award of UK/EU rate fees plus stipend.

Award Value

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.

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 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 (in English).

The following skills are desirable but not essential: 

  • Experience of data analysis or modelling.

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 19ENGIN_SRMO in all correspondence and in your graduate application. Informal enquiries should be addressed to Prof. Steve Roberts (sjrob@robots.ox.ac.uk) or Prof. Mike Osborne (mosb@robots.ox.ac.uk).

Application deadline: 12 noon UK time on Friday 25th January 2019

Start date: normally October 2019