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, and statistic are merits as well as documented knowledge in microbial ecology or ecology. You should show a keen interest in learning machine-learning and other AI methods. The working language is
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Do you want to contribute to improving human health? Decscription of the researchgroup Our lab is advancing precision cancer medicine through deep learning models of the molecular networks within
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learning methodologies to extract conformational ensembles from single-particle cryo-EM data. The project builds on our recently established (not yet published) software, which machine-learns protein
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. This research project is a collaboration between Eric Libby and Laura Carroll. It involves using machine learning techniques to infer the mechanisms by which microbes make decisions, bioinformatic techniques
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, molecular omic, and neuroimaging data using machine learning Project: Stroke is a leading cause of death and disability worldwide. Ischemic stroke is heterogeneous both in terms of etiology and outcomes
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neurodevelopmental reconstruction using a machine learning approach and beyond. Combo of single cell and spatial transcriptomics adds a critical layer of information by mapping gene expression patterns to specific
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presentations. You will be supervised researchers who collectively offer expertise in computational biology, genetics, epidemiology, and machine learning. The research will be closely linked to the WISDOM project
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trials, cutting edge genomics, machine learning and artificial intelligence. The doctoral student project and the duties of the doctoral student At the Department of Medical Epidemiology and Biostatistics
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: have experience working on machine learning systems in PyTorch or JAX have experience in interpretable methods in machine learning have experience in using version control systems like Git and public
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environment, and, preferably, previous experience in working with machine learning and/or artificial intelligence. Good presentation and communication skills in oral and written English are also required