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Field
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. Experience with statistical modelling, signal processing, or machine learning. Interest in active perception, surface exploration, and computational neuroscience. Eligibility criteria Applicants must satisfy
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/ESA_selects_Harmony_as_tenth_Earth_Explorer_mission ) The candidate will develop and apply cutting-edge remote sensing or photogrammetry methods, in combination with data science approaches such as machine learning and data assimilation via
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methods in meteorology (atmospheric data assimilation). This contributes to the cutting-edge research expertise of the College and the University in the strategic areas of Machine Learning, Statistical Data
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. · Knowledge of molecular biology, cell biology, immunology, neuroscience, or ophthalmic research is considered an asset. · Experience with single-cell genomics, spatial transcriptomics, machine learning
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, or ophthalmic research is considered an asset. · Experience with single-cell genomics, spatial transcriptomics, machine learning, artificial intelligence, or translational biomedical research is considered
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of future reactor systems with a focus on systems relevant for Norway. The objective is to further develop and validate machine-learning surrogate models derived from high-fidelity multiphysics simulations
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to the Terascale Project Knowledge of research methods relevant to AI for materials science Knowledge of one or more of the following areas: machine learning interatomic potentials, generative AI, automated and
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submitted a relevant PhD and have expertise in behavioural science, AI, machine learning and/or an intersection of those fields. This expertise should be explained in a letter of application and demonstrated
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, chromatin profiling, genomics, spatial transcriptomics and single-cell data. Apply statistical, machine learning, and network-based approaches to analyze high-dimensional biological data. Collaborate closely
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programming languages. Experience with DICOM data, medical-image registration, high-performance computing, or GPU-based computation. Familiarity with machine-learning or deep-learning methods for medical-image