Postdoctoral Research Assistant in Machine Learning
University of Oxford - Department of Engineering Science
|Salary:||£31,076 to £38,183 Grade 7 p.a.|
|Contract Type:||Contract / Temporary|
|Placed on:||21st November 2016|
|Closes:||18th January 2017|
We are seeking a full-time Postdoctoral Research Assistant in Machine Learning for Finance. The successful applicant will join the Machine Learning Research Group at the Department of Engineering Science and the Oxford-Man Institute of Quantitative Finance (central Oxford). The post is part of a collaborative project with Man and AHL, which aims to develop next-generation time-series models using scalable Bayesian models. The post is fixed-term for 36 months.
You will be responsible for research into time-series modelling techniques, with a particular focus on changepoint detection algorithms, as well as developing methods for forecasting, information aggregation from heterogeneous sources and scalability.
You should possess a good first degree in information engineering, physics, computer science, mathematics, statistics or similar, with specialisation in time series and sequential data analysis and have, or are about to complete, a PhD in a relevant area. Experience in Bayesian inference and practical applications in uncertain domains as well as expertise and experience in computer programming are essential. Experience in the applications of machine learning methods in finance is desirable. You should have a track record of published work concomitant with experience and the ability to work well independently and as part of a team.
Only applications received before 12.00 midday on 18 January 2017 can be considered. You will be required to upload a covering letter/supporting statement, including a brief statement of research interests (describing how past experience and future plans fit with the advertised position), CV and the details of two referees as part of your online application.
The department holds an Athena Swan Bronze award, highlighting its commitment to promoting women in Science, Engineering and Technology.
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South East England