We are seeking two Postdoctoral Research Assistants in Machine Learning to join Professor Torr’s research group at the Department of Engineering Science (central Oxford). 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 https://torrvision.com/. The posts are fixed-term for 2 year in the first instance with funding provided by the EPSRC.
You will be responsible for the development and implementation of novel computer vision and learning algorithms for reliable, robust, and efficient deep neural networks.
You should hold a PhD or DPhil (or be near completion of) in Computer Vision or Machine Learning. You should also have excellent communication skills, including the ability to write for publication, present research proposals and results, and represent the research group at meetings.
Informal enquiries may be addressed to email@example.com.
The University offers a comprehensive range of childcare services and has very generous maternity, adoption, paternity, shared parental leave schemes in operation. Requests for flexible working are always taken into consideration. We offer an enhanced entitlement to 38 days’ annual leave per year (pro-rata for part-time staff), inclusive of bank holidays and fixed closure days. Additional long service leave is available after 5 years’ service. An additional scheme enables staff to request to purchase up to ten additional days’ annual leave in each holiday year. Other staff benefits can be found here https://hr.admin.ox.ac.uk/staff-benefits
Only applications received before midday on the 16th February 2022 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.