132 computational-mechanics Postdoctoral positions at University of Oxford in United Kingdom
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We are seeking five full-time Postdoctoral Research Assistants to join the Computational Health Informatics Lab at the Department of Engineering Science, based at the Institute of Biomedical
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on evaluating the abilities of large language models (LLMs) of replicating results from the arXiv.org repository across computational sciences and engineering. You should have a PhD/DPhil (or be near completion
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shelves, the breakup of which can speed up flow of grounded ice and affect global sea level, and on the highly specialised Antarctic biodiversity. This ambitious programme brings together leading UK (BAS
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on the mechanisms and consequences of immune escape in colorectal cancers (CRC) and pre-cancers. You will manage your own academic research and administrative activities. This involves small scale project management
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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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to unravel the molecular mechanisms behind these aging-related phenotypes. A willingness to learn and employ biochemical approaches and interact with external collaborators in molecular biology and
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graduate students; About the project During our lifetimes, we copy approximately a lightyear’s worth of DNA, and how the different components of the molecular machinery (the replisome) work together to
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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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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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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