609 parallel-and-distributed-computing-phd Postdoctoral research jobs in United Kingdom
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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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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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the risks. You will have: a PhD in one of the relevant STEM disciplines, such as mathematics, statistics, computer sciences, theoretical food, ecological or physical sciences, etc. skills in mathematical
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About the Role You will develop and apply novel computational methods to quantify the societal impact of fundamental science discoveries. Candidates close to completion of their PhD will initially
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-term persistent infections, as well as on newly emerging variants of concern (VOCs). We are seeking a highly motivated candidate that has obtained (or will obtain soon) a PhD in a relevant subject (i.e
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research programme at Oxford. Candidates should hold a PhD in biomedical engineering, computer science, medical physics, statistics, or a related field. A strong track record of first-/senior or co-author
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research programme at Oxford. Candidates should hold a PhD in biomedical engineering, computer science, medical physics, statistics, or a related field. A strong track record of first-/senior or co-author
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programme. The particular emphasis of the project will be to examine the interaction between atherosclerotic plaque macrophages and neutrophil extracellular traps and how it impacts cellular function and
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scholars in Law and two PhD students (one in Law and one in Computer Science/Data Analytics), as well as with international, European and national stakeholders involved in the CURE project. The post-holder
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experience in: Deep learning Medical imaging computing (preferably neuroimaging) Computationally efficient deep learning Deep learning model generalisation techniques. Translating deep learning models