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# Postdoctoral Research Assistant in Machine Learning

- **Company:** University of Oxford
- **Location:** Oxford, UK
- **Contract:** Permanent contract
- **Skills:** Computer Vision, Machine Learning, Reinforcement Learning, Deep Learning
- **Published:** October 24, 2026
- **Apply:** https://my.corehr.com/pls/uoxrecruit/erq_jobspec_version_4.display_form?p_company=10&p_internal_external=E&p_display_in_irish=N&p_process_type=&p_applicant_no=&p_form_profile_detail=&p_display_apply_ind=Y&p_refresh_search=Y&p_recruitment_id=188987

## About the Role

You should hold a relevant PhD/DPhil (or near completion*) in Machine Learning, Computer Vision, Robotics or a related field. You should have a strong publication record at leading international conferences in these areas, together with sufficient theoretical and practical knowledge of deep learning methodologies. Research experience in reinforcement learning, computer vision or robotics would be desirable.

## Description

We are seeking two full-time Postdoctoral Research Assistants in Machine Learning to join the Visual Geometry Group (VGG), in central Oxford. The positions are associated with two separate research projects funded by Toyota Motor Europe and Amazon, respectively. Each successful candidate will be assigned to one of these projects and will work under the supervision of Dr. João Henriques. Both posts are available for 12 months in the first instance.
The Toyota Motor Europe project, RoboSim, involves research in deep learning, at the intersection of reinforcement learning and computer vision, to train effective robotic agents in simulation. The research will focus on developing robust visuomotor policies that can transfer to real robots, in collaboration with researchers at the Oxford Robotics Institute and Toyota Motor Europe.
The Amazon project involves research on robotic manipulation using computer vision and vision-language-action models. The research will investigate how representations of 3D geometry and motion can help robots manipulate a wide range of objects, including deformable objects, and generalise to new settings. The methods will be evaluated on robotic manipulation tasks, including experiments on real robots.