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quantitative field. Strong background and expertise in data science, bioinformatics, network science, artificial intelligence, machine learning, deep learning, or related areas. Solid understanding of AI
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in resulting companies, etc. The work will comprise machine learning research for analysing large-scale clinical data, including time-series physiological data, blood test data, medications
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interests in applied statistics, machine learning, or computational biology are encouraged to apply. For more information, please visit our website https://ds.dfci.harvard.edu/postdocs to view the list
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will also profit from the vibrant research community around machine learning of the SCADS.AI center (https://scads.ai ) and the recently granted Excellence Cluster REC² – Responsible Electronics in
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cutting-edge research in areas such as pattern recognition, automation science, complex systems, AI for Science, robotics, machine learning, computer vision, natural language processing, biometrics, medical
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, scale and resolution in which in vivo pathways of immune cells can be unraveled. Furthermore, it provides a goldmine for training causal machine learning models to move towards precision medicine
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plate array microscope for simultaneous time-lapse video microscopy, enabling high-throughput single-cell analyses of rapidly migrating cells. You will be responsible for Developing new machine learning
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, machine learning, and data-driven modeling methods, physiology, transport, fluid and solid mechanics, systems analysis, circuit prototyping, technology transfer, and biomedical design practices, in
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, or methodologies in bioengineering. This search has a particular focus in immunology, neuroscience, and/or computational science/machine learning. That said, we give high priority to the overall originality and
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infrastructures organized in infrastructure platforms, of which the Vibrational Spectroscopy Core Facility (ViSp) is a central infrastructure for this project (https://www.umu.se/en/research/infrastructure/visp