Research Associate on Data Simulation using Generative Adversarial Network

In collaboration with the Alan Turing Institute (ATI), University of Bristol invites applications for a Research Associate to work on a project on Data Simulation using Generative Adversarial Networks (GANs), which is a very active research area in machine learning. Funding for this position is provided by the Turing Institute available for one year.

This is a new post funded by the Turing Institute's Pilot Project addressing the interpretability of high dimensional datasets using deep learning generative models. This project led by Turing fellow Dr. Song Liu explores the ways of using GANs to understand the uncertainty and structure of the underlying distribution of high dimensional datasets without explicitly using a probabilistic model. In contrast to complex models, simulated samples have the advantage of being more intuitive and explainable.

The successful candidate will work on developing GAN algorithms that simulates high dimensional samples that have certain “traits” which make them good “statistical representatives” of the dataset. The candidate will not only have opportunity to work with Dr. Song Liu in Intelligent System Group at University of Bristol but will also be able to spend time at the Turing Institute and work with relevant people there.

Candidates are expected to have expertise in machine learning and deep generative models. A PhD in relevant fields is highly desirable.  If you have any questions about this vacancy, please contact Dr. Song Liu (song.liu@bristol.ac.uk) for informal inquires.

This position is being offered on a full time, fixed term contract for up to 12 months.

Closing Date: Sunday 17th Feb 2019, with interview in late February or early March with a start date as soon as possible after.

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