Grade 7: £32,817 - £40,322 p.a.
Mar 12, 2020 12:00PM
We are seeking a full-time Postdoctoral Research Assistant to join Professor Torr’s research group at the Department of Engineering Science (central Oxford). This post is funded by an industry partner and is fixed-term for 24 months. The research group is internationally leading, with numerous scientific awards and has close links with some of the top industrial research labs, more information can be found here www.robots.ox.ac.uk/~tvg/.
The objectives of the project are to design efficient mathematical models, primarily based on Neural Networks that can automatically identify and regulate DeepFakes (manipulated media using deep learning) at scale. You will be responsible for the development and implementation of novel computer vision and learning algorithms for understanding Deep Fakes. There is a large scope for academic freedom, the applicant would be advised to study recent research publications to get an idea of the work directions which include, deep generative models such as GANs and VAEs (and their hybrid forms), model generalisation, verification, softbiometric signatures, and various training tricks (weight normalisation, batch norm, low/high learning rate, effect of optimisers etc.).
You should possess a doctorate, or be near completion of doctorate, in computer vision or machine learning, together with a strong publication record at principal computer vision or machine learning conferences (NIPS, ICLR, ICML, CVPR, ECCV, ICCV), with a background in machine learning.
Informal enquiries may be addressed to Professor Torr using the email address below.
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.
Only applications received before 12.00 midday on 12 March 2020 can be considered.
The Department holds an Athena Swan Bronze award, highlighting its commitment to promoting women in Science, Engineering and Technology.
Professor Philip Torr