Postdoctoral Research Assistant in Machine Learning

at  University of Oxford

We are seeking a full-time Postdoctoral Research Assistant in Machine Learning to join Torr Vision Group at the Department of Engineering Science (central Oxford). The post is funded by Microsoft and is fixed-term for 3 years until April 2027.
This is an industry collaboration project aimed at advancing the research into the use, extension and optimization of multi-modal large language models (LLMs) and related architectures for generative tasks, continuous learning, indexing or retrieval, support of retrieval augmented generation over many data points from long term histories, automatic extraction of relevant data from noisy observations.
You will be responsible for developing and implementing novel methods for screen and video understanding, user interaction understanding, planning and intent prediction with multimodal LLM and SLMs. Planning for user data collection for video snapshots of diverse set of users performing a range of workflows using Windows system including considerations for privacy, security and data-richness.
You should possess a PhD or DPhil (or near completion of) in Computer Vision or Machine Learning. You should have knowledge of approaches for areas related to efficient, reliable, and robust deep neural networks applied to computer vision tasks. You should have the ability to manage your own academic research and associated activities.
Informal enquiries may be addressed to Professor Philip Torr (email: philip.torr@eng.ox.ac.uk)
For more information about working at the Department, see www.eng.ox.ac.uk/about/work-with-us/
Only online applications received before midday on 4th April 2024 can be considered. 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.
The Department holds an Athena Swan Bronze award, highlighting its commitment to promoting women in Science, Engineering and Technology

Please refer the Job description for details