Postdoctoral Research Assistant in Machine Learning Closing date: Sep 05, 2025 Sep 05, 2025

Postdoctoral Research Assistant in Machine Learning

Grade 7: £38,674 - £46,913 per annum (Inclusive of Oxford University weighting)

DescriptionWe are seeking a full-time postdoctoral researcher to join Torr Vision Group at the Department of Engineering Science (central Oxford). The post is funded by EPSRC and is fixed-term for one year with the possibility of renewal.

This project addresses the high computational and energy costs of Large Language Models (LLMs) by developing more efficient training and inference methods, particularly in continual learning settings. The core focus is on leveraging Reinforcement Learning (RL) to make the training and deployment of LLMs more computationally and sample efficient. This approach aims to improve the accessibility, scalability, and sustainability of LLMs. The research will span foundational advances in RL/LLM integration to applied work with potential real-world impact.

You will conduct cutting-edge research at the intersection of RL and LLMs. You will also design and run experiments to improve LLM efficiency and sustainability.

You will hold a relevant PhD/DPhil or be near completion together with relevant experience. You will have a strong technical background in machine learning, especially RL and LLMs. An ability to work independently and as part of a collaborative research team is essential.

Informal enquiries may be addressed to Dr. Yangchen Pan:

For more information about working at the Department, seewww.eng.ox.ac.uk/about/work-with-us/

Only online applications received before midday on Sept 5st, 2025 can be considered. You will be required to upload supporting statements, including a brief statement of research interests (describing how past experience and future plans fit with the advertised position, two pages recommended), 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.

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