Postdoctoral Research Assistant in Machine Learning, University of Oxford

We are seeking a full-time Postdoctoral Research Assistant in Machine Learning to research next generation methods in multi-sensor data analysis and spatio-temporal modelling. The successful applicant will join the Machine Learning Research Group at the Department of Engineering Science, Central Oxford. The post will develop next-generation disaster prevention and environmental protection situation awareness technology (www.gov.uk/government/case-studies/airbus-africa-flood-draught-resilience and www.easos.my). The post is fixed-term for 28 months or until the 31 November 2019 (whichever comes first).
You will be responsible for research into scalable, mixed-source data fusion techniques, with a particular focus on classifier combination algorithms, as well as developing methods for human-agent collectives, crowdsourcing and scalable inference techniques for spatio-temporal modelling.
You should possess a good first degree in information engineering, physics, computer science, mathematics, statistics or similar, with specialisation in machine learning and have, or are about to complete, a PhD in a relevant area. Experience in Bayesian inference and practical applications in domains with uncertain information as well as expertise and experience in computer programming are essential. Experience in the applications of machine learning methods (including deep neural networks) in geographic information systems 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.
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.
Further information from http://www.eng.ox.ac.uk/jobs/current-vacancies/vac...
Only applications received before 12.00 midday on 14 July 2017 can be considered