Postdoctoral Research Assistant in Machine Learning by Bayesian Optimisation for Experimental Research in Quantum Nanodevices

University of Oxford - Department of Materials

Parks Road, Oxford

We are seeking to appoint a Postdoctoral Research Assistant whose aim will be to harness Machine Learning techniques for the process of scientific discovery. Duties will include development and application of Bayesian Optimisation for measurements of single-molecule devices, and training them on simulated experimental data. The post is available for up to 3 years and is under the supervision of Professor Andrew Briggs.

The project's overarching aim is to identify properties of molecular systems that are desirable in future information processing, especially lower power switching to minimise energy costs (and consequent environmental impact). You will engage and work collaboratively with others involved in the programme including Professor Michael Osborne, Department of Engineering Science, who will supervise the development of the machine learning methods.

You will have a good first degree and a completed doctorate (or nearly completed) in a relevant discipline. You will have expertise and experience in software engineering, along with demonstrated expertise in model-based machine learning. The ability to start not later than 9 January 2017 would be desirable.

The Department of Materials is actively promoting the provision of a family friendly working environment and together with the University of Oxford recognises the demands of work/life balance. Therefore for this project we encourage applications from candidates who wish either to hold these positions on a full-time, or part-time basis or need flexibility in their working hours and will discuss these opportunities with shortlisted applicants at interview.

The closing date for applications is 12.00 midday on 31 October 2016.

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