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are seeking a postdoctoral research associate to lead an innovative EU-funded project at the intersection of polymer chemistry, computational modelling, and machine learning. The primary role is to develop a
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innovative EU-funded project at the intersection of polymer chemistry, computational modelling, and machine learning. The primary role is to develop a complete in silico framework to accelerate the discovery
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and trustworthy machine learning-based clinical prediction models. Funded by the Medical Research Council (MRC) and the National Institute for Health and Care Research (NIHR), the project aims
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-performance or cloud computing environments. Need strong data management and database skills, expertise in clinical phenotyping ontologies and the application of machine-learning/AI methods to biomedical data
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To undertake research on the evolution of prokaryotic pangenomes using machine learning and AI approaches. The work will involve the analysis of large prokaryotic genome datasets, the development
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-performance or cloud computing environments. Need strong data management and database skills, expertise in clinical phenotyping ontologies and the application of machine-learning/AI methods to biomedical data
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innovative EU-funded project at the intersection of polymer chemistry, computational modelling, and machine learning. The primary role is to develop a complete in silico framework to accelerate the discovery
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, or a related field. Candidates must possess relevant research experience in probability, stochastic analysis, and optimization. Applicants with knowledge in machine learning, as well as a track record
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(postdoc) Limited until: permanent Reference no.: 4984 Among the many reasons to research and teach at the University of Vienna there is one in particular, which has convinced around 7,500 academic staff
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classification algorithms including machine learning); and the output data and interpretability. The project “SORS in the community” is funded by the EPSRC (https://www.ukri.org/news/new-tools-aim-to-improve-early