Research Associate in Machine Learning
University of Bristol School of Mathematics
United Kingdom

Town/City:
Bristol
Position type:
Part time
Salary:
£33,199 – £37,345 per annum pro rata
POSTED 26 Aug 2019
EXPIRES 24 Sep 2019


FULL DETAILS:
Reference number: BRI-ACAD104172

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. If you have any questions about this vacancy, please contact Dr. Song Liu (song.liu@bristol.ac.uk) for informal inquries. Please upload in the online application (1) your CV; (2) a Covering Letter of no more than 1,500 words and explaining how your skills would contribute to this project.

We welcome applications from all members of our community and are particularly encouraging those from diverse groups, such as members of the LGBT+ and BAME communities, to join us.


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