Description: We are seeking a full-time postdoctoral research assistant to join Professor Torr’s research group at the Department of Engineering Science (central Oxford). The post is funded by an EU project Helios, Global Scene Understanding with Energy Models. It is fixed-term for 2 years. The group is an internationally leading research group that has 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/.
You will be responsible for the development and implementation of novel computer vision and learning algorithms to automate semantic SLAM via reinforcement learning. The aim of this work is to apply techniques developed in deep reinforcement learning for playing games to develop new state of the art Semantic SLAM systems, inspired by recent successes from Google DeepMind on using these techniques to navigate computer game mazes. For a strong candidate there is flexibility on topic, and opportunity to supervise PhD students.
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, with a background in one of graphical models, combinatorial optimisation, semantic segmentation and machine learning. In particular we are looking for someone who has worked and innovated in reinforcement learning; as well as someone who has worked on holistic scene understanding but other top rated research will definitely be considered.
Informal enquiries may be addressed to Philip Torr at 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 Wednesday 2 August 2017 can be considered.
The Department holds an Athena Swan Bronze award, highlighting its commitment to promoting women in Science, Engineering and Technology.