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have completed, or be close to completing, a PhD/DPhil in a relevant quantitative field such as computational social science, computer science, or cognitive science. They will have a demonstrable track
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computational workflows on a high-performance cluster. You will test hypotheses using data from multiple sources, refining your approach as needed. The role also involves close collaboration with colleagues
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We are looking to appoint a postdoctoral researcher, to work with a group of UK Higher Education Institutions to deliver a programme of mental health research. The work is funded by the Medical
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Postdoctoral Research Associate in Sum-Frequency Generation Microscopy of Biomolecular Self-Assembly
can apply for this position by submitting the following documents through the MPG postdoc program website (https://postdocprogram.mpg.de/node/21092 ): Motivation letter detailing your background, reason
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This post is a postdoctoral research assistant role within Prof Robert House’s Group in the Department of Materials. The post will be fixed-term until 31 March 2027 (with potential to extend until 31 September 2028) in association with a new Faraday Institution-funded project entitled...
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learning, at the intersection of reinforcement learning, deep learning and computer vision, in order to train effective robotic agents in simulation. You should hold a relevant PhD/DPhil (or near completion
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Programme Manager, using the contact details below. Only online applications received before 12.00 midday on 29th August will be considered. Interviews will be held as soon as possible thereafter
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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
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and Prof Paul Shearing. The post is funded through a strategic research partnership and is fixed term for up to 2 years. To support the programme, the post holder will be required to carry out research
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research initiative funded by ARIA, titled Aggregating Safety Preferences for AI Systems: A Social Choice Approach. The project operates at the interface of AI safety and computational social choice, and