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with an international reputation for excellence. The Department has a substantial research programme, with major funding from Medical Research Council (MRC), Wellcome Trust and National Institute
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with an international reputation for excellence. The Department has a substantial research programme, with major funding from Medical Research Council (MRC), Wellcome Trust and National Institute
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well as access to the group dedicated computing cluster environment with H100, L40s, and A40 GPUs. This post is funded by the UKRI Future Leaders Fellowship, a flexible long-term public funding scheme
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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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numerical modelling. Ability to participate in research meetings to troubleshoot research problems and discuss the direction of research. Skills Fluency in at least one scientific computer language (e.g., C
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Full time: 25 Hours per week Fixed term: 12 months We are looking for a candidate to join the University of Edinburgh to conduct research on Machine Learning, Reinforcement Learning, or LLM orchestration, with applications to Finance. This post is advertised as full-time (35 hours per week). We...
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About the role The Kelly lab is excited to announce a new post-doctoral position in computational biology. This position is funded as part of an international consortium of scientists who
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qualification/experience equivalent to PhD level in a relevant subject area (physics, engineering, computing science, etc.). You will need as essential skills a good knowledge of C and python, familiarity with
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qualification/experience equivalent to PhD level in a relevant subject area (physics, engineering, computing science, etc.). You will need as essential skills a good knowledge of C++ and python, familiarity with
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Postdoctoral Researcher in Machine Learning of Isomerization in Porous Molecular Framework Materials
broad range of applications. Computational chemistry and Machine Learning increasingly underlies MFM research to search or screen candidate MFMs prior to synthesis. A major drawback when applying