424 computer-programmer-"U.S"-"University-of-St"-"U" Postdoctoral positions in United Kingdom
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including cell culture, organ-chip models, tissue engineering, and musculoskeletal biology. The PDRA will plan and conduct experiments, generate high-quality data, prepare publications, make presentations and
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well as clinical phenotyping in participants. The work will involve close collaboration with other team members, driving the programme on aetiopathology of neuropsychiatric disease forward. It will also involve
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of genome organisation and metabolic control—with the bold vision of building synthetic life. In this role, you will develop and apply computational methods to analyse single-cell modalities, focusing on gene
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sport and gym facilities and access to a 24-7 Employee Assistance Programme. • On site nursery is available plus access to holiday camps for children aged 5-16. • Family friendly policies, including
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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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exploitative trade with African nations and links to colonial enterprise. Following our February 2022 Race Review and our Race Equity Action Plan, LSTM committed to becoming an actively anti-racist organisation
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. Ready to be part of our team? Let’s shape the future together! About the team: The Computational Materials Discovery group is looking for a postdoctoral researcher working in the field of machine learning
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including discounted membership for our state of the art sport and gym facilities and access to a 24-7 Employee Assistance Programme. • On site nursery is available plus access to holiday camps
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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