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. The Proteomics unit in UiO has state-of-the art facilities for modern mass spectrometry-based proteomics, and there is strong focus on proteomics method development by multiple partner groups in COMBINE
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in deep learning methodology for earth observation. You will work on deep learning methods for detection of oil spills in the ocean based on satellite imagery. A key focus will be to develop oil spill
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these challenges by advancing machine learning (ML)-based and data-intensive methods for the scalable design, processing, and deployment of DPPs. The advertised PhD position focuses on developing ML-based and
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engaged in teaching and research covering a wide spectrum of subjects within mathematics, mechanics and statistics. The research is on theory, methods and applications. The areas represented include: fluid
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computational methods for antibody recognition is crucial for the development of personalized and precision medicine approaches such as next-generation infection, cancer, and autoimmune immunodiagnostics and
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mathematics, mechanics and statistics. The research is on theory, methods and applications. The areas represented include: fluid mechanics, biomechanics, statistics and data science, computational mathematics
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observation for oil spill monitoring You are keen on contributing to new advances in deep learning methodology for earth observation. You will work on deep learning methods for detection of oil spills in
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programming and computational modelling as core elements. Questions about the position For further information please contact: Anthony Mathelier Assistant Centre Director anthony.mathelier@ncmbm.uio.no
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programming and computational modelling as core elements. Questions about the position For further information please contact: Anthony Mathelier Assistant Centre Director anthony.mathelier@ncmbm.uio.no
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triggers new questions about the locus of agency and learning these emergent collaboration ecologies. Such examinations may require in-depth qualitative analyses but also mixed-methods approaches, possibly