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developing machine learning or data science approaches for patient stratification and genetic association analyses using cardiac magnetic resonance imaging in biobank populations. Successful applicants will
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EMBL-EBI - European Bioinformatics Institute | Hinxton, England | United Kingdom | about 1 month ago
of the Saez Rodriguez group is to acquire a functional understanding of the deregulation of signalling networks in disease and to apply this knowledge to develop novel therapeutics. We focus on cancer, auto
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verification of machine learning models, and conformal inference. Applicants should demonstrate scientific creativity, research independence, the capacity to support junior team members, and strong communication
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of integrating advanced optical technologies with machine learning techniques to develop novel, high-performance fibre-optic sensing applications. You will be responsible for the application and validation
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environment. In this role, you will lead the computational strand of the project, applying molecular simulations, data analysis, and machine learning to uncover how molecular structure, charge, and surface
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candidate will be at the forefront of integrating advanced optical technologies with machine learning techniques to develop novel, high-performance fibre-optic sensing applications. You will be responsible
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Introduction As a University of Applied Learning, SIT works closely with industry in our research pursuits. Our research staff will have the opportunity to be equipped with applied research skill
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of manufacturing. We have identified an opportunity to combine continuous microfluidic (µF) process models, process analytical technology (PAT) and machine learning (ML) to achieve a paradigm shift in bioprocess
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the next generation of gas turbine engines. Successful candidates will have a PhD or equivalent in a relevant discipline and experience in the development of machine/deep learning (ML/DL) methods
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of manufacturing. We have identified an opportunity to combine continuous microfluidic (µF) process models, process analytical techn ology (PAT) and machine learning (ML) to achieve a paradigm shift in bioprocess