310 computer-programmer-"https:"-"FEMTO-ST"-"https:"-"https:"-"https:" Postdoctoral positions in United Kingdom
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Postdoctoral Researcher. The group aims to identify, understand, and develop therapies for rare genetic disorders. The group is primarily computational but partners with multiple international labs (including
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Ref.: 533742 Work type: Full-time Department: School of Computing and Data Science (14700) Categories: Senior Research Staff & Post-doctoral Fellow Post-doctoral Fellow in the Department
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About the department Our vibrant Faculty of Media, Science & Technology encompasses a wide range of disciplines across four Schools: the School of Computing & Engineering, the National Centre
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an interdisciplinary team led by Dr Peter Etchells. The successful applicant will be expected to plan and perform genetics, gene expression, and phenotyping aspects of the project. Key responsibilities: To understand
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record in a related field, and possess sufficient specialist knowledge in the discipline to work within established research programmes. You will have the ability to identify research objectives and to
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an interdisciplinary team led by Dr Peter Etchells. The successful applicant will be expected to plan and perform genetics, gene expression, and phenotyping aspects of the project. Key responsibilities: To understand
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handling of anhydrous hydrogen fluoride and the development of physical organic models to assess their reactivity. High level computational techniques will be used to aid understanding. This exciting
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the essential criteria, and how you plan to use the MIGHTEE data alongside the spectroscopic data from 4MOST and MOONS for your research. Your referees should be asked to send their letter, to **Leanne O’Donnell
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an interdisciplinary team working across physical and computer science to develop new pathways to the design and discovery of inorganic materials as part of the Leverhulme Centre for Functional Materials Design. This
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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