283 computer-programmer-"https:"-"INSA-toulouse"-"https:"-"https:"-"https:" Postdoctoral positions in United Kingdom
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Hopkins University (USA). About the role The PDRA will lead the development of new computational and mathematical models to quantify and predict infectious disease risk, particularly for identifying high
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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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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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University (USA), and Google Deepmind (London). About the role The PDRA will lead the development of new computational and mathematical models to quantify and predict infectious disease risk, particularly for
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and of research methods and techniques to work within established research programmes. Applicants will be able to perform ultrafast optical measurements, integrate items of advanced instrumentation, and
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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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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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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