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membrane proteins involved in numerous physiological processes. By leveraging machine-learning enhanced virtual screening, the PhD student will be able to perform searches for ligands in chemical libraries
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subjects are valued: optimisation, probabilistic machine learning, linear algebra and deep learning. Rules governing PhD students are set out in the Higher Education Ordinance chapter 5, §§ 1-7 and in
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knowledge in microbial ecology or ecology. You should show a keen interest in learning machine-learning and other AI methods. The working language is English and excellent communication skills in English
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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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opportunity as an Industrial PhD student to work on a project that applies machine learning to improve diagnostic and reporting workflow processes in clinical kidney pathology. This project is a collaboration
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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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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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molecular biology are desirable but not necessary, as well as interest in and knowledge of machine learning, bioinformatics, and data processing. The PhD student is expected to play an active role in
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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