64 distributed-algorithm-"Fraunhofer-Gesellschaft" Fellowship research jobs in United Kingdom
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learning methods, such as longitudinal analysis methods, latent variables models, clustering algorithms, missing data and clinical trial analysis Strong publication record Experience in women and children’s
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highly motivated researcher to develop artificial intelligence based novel algorithms and computational workflows to predict the impact of mutations on genes in the avian flu virus and the viral host which
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binding pockets. About the role We are seeking a highly motivated researcher to develop artificial intelligence based novel algorithms and computational workflows to identify domain functional families
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in implementing, testing and validating complex minimisation algorithms that can be used for adaptive trials. Application & interview 8 Experience of collaborating on successful research proposals
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will undertake original research of international excellence. It will be key that you prepare your work for publication in leading journals and contribute to its distribution at national and
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impacts, or inequalities in their distribution. The new Hub will identify barriers, incentives and accelerants to implementing healthy low-carbon transport schemes and propose and evaluate new solutions
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of Earth and Environment (SEE), and working closely with its School of Civil Engineering, you will use various seismic datasets – including data already recorded with fibre-optic distributed sensing systems
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bioinformatics Previous experience with AI and/or machine learning approaches Interest in reproductive health and/or development of clinical tools and algorithms Downloading a copy of our Job Description Full
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work alongside renowned academics and researchers in ENU’s School of Computing, Engineering and the Built Environment. If you are someone with expertise in multimodal speech processing and AI algorithms
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measures have not systemically considered physical and mental health impacts, or inequalities in their distribution. The new Hub will identify barriers, incentives and accelerants to implementing healthy low