490 parallel-computing-numerical-methods Postdoctoral research jobs in United Kingdom
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science, with applications to public and veterinary health, therapeutic development, and pandemic preparedness. You will contribute to method development, data analysis, and collaborative projects, and will
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the research group, as appropriate, to identify areas for research, develop new research methods and extend the research portfolio. To deal with problems that may affect the achievement of research objectives
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, state-of-the-art laboratories, high-performance computing, and industry collaboration through the London Institute for Healthcare Engineering. About the role We are looking for a highly motivated
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the possibility of extension subject to funding. The UK Programme of Laser Inertial Fusion Technology for Energy (UPLiFT) is a 4 year £10M research programme funded by the UK government’s Department for Energy
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standard imaging analysis method including use of Python (NumPy/SciPy/PyTorch/Tensorflow), Matlab, C++, version control software (e.g. git), and statistical analysis using R, SQL, etc. Familiarity with
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standard imaging analysis method including use of Python (NumPy/SciPy/PyTorch/Tensorflow), Matlab, C++, version control software (e.g. git), and statistical analysis using R, SQL, etc. Familiarity with
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of Engineering Science (central Oxford). The post is funded by the EPSRC research funds and is fixed-term for up to one year. The successful applicant will have experience on using sequential decision-making methods
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-on experience in ceramic/polymeric membrane processing, familiarity with membrane characterization methods, and strong data analysis and communication abilities. Experience with scaling up membrane technologies
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on single-agent settings. We are seeking a highly motivated postdoc to conduct research into this fast-moving area. Directions may include investigating quality evaluation methods for multi-agent systems
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and realign how we measure and model populations by infusing new types of data, methods and unconventional approaches to tackle the most challenging demographic problems of our time. We are seeking a