Postdoctoral Research Assistant in Scalable Probabilistic Data Analytics
*Applications to be received by 12pm on Wednesday 13th of January 2016* *Grade 7: £30,738 - £37,768 p.a.*
We are seeking a full-time Postdoctoral Research Assistant in Scalable Probabilistic Data Analytics to join the Machine Learning research group at the Department of Engineering Science, central Oxford. The post is funded by the Oxford-Emirates Data Science Lab and is fixed-term for 3 years.
The Oxford Researcher will be drawn from areas of Mathematics and/or Information Engineering (Machine Learning and Artificial Intelligence).
They will spend approximately 50% of their time working on Emirates-focused research and will act as a bridge between the Emirates-focused research and Oxford research groups within their own departments. They will be responsible for efficient non-parametric Bayesian sequential inference; online anomaly, changepoint and fault detection; structure learning for high-dimensional time-series data.
They should possess (or near completion of) a PhD in a relevant area, as well as a good first degree in engineering, mathematics/statistics, computer science or equivalent, with specialisation in probabilistic inference. You must have experience in Bayesian inference and machine learning including previous experience with the practical implementation of Bayesian non-parametric models on real-world data. Expertise and experience in computer programming as well as the ability to work independently and as part of a team are essential.
Further information can be found at: www.eng.ox.ac.uk/jobs/home
Only applications received before 12.00 midday on 13 January 2016 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.
