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searching and integration. Maintains the computational infrastructure and tracks the flow of samples and information for large-scale studies. Provides web-based bioinformatics and access to public and
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computational sub-team that includes computer scientists and computational PhD students, fostering an interactive environment of technical exchange, code review, mutual support, and collaborative problem-solving
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to staff position within a Research Infrastructure? No Offer Description PhD Position in Physics-Informed Machine Learning for Cardiac Magnetic Resonance The CMR Zurich group at the Institute
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include data compression and reconstruction, data movement, data assimilation, surrogate model design, and machine learning algorithms. The position comes with a travel allowance and access to advanced
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machine-learning tools. Data analyzed include precursors such as volatile organic compounds, aerosol number and mass concentrations, chemistry, biological particles, cloud and ice condensation nuclei, light
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, synaptic growth, brain network organization and connectivity, cognitive function) Using advanced neuroimaging and/or machine learning techniques to understand the connection between physical activity
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mathematical foundations of data science, machine learning and/or artificial intelligence. Preference will be given to candidates studying either the application of data science/ML/AI to problems in
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analysis and biomedical data analysis, with demonstrated experience in organ segmentation from medical images, using both traditional and machine learning–based methods, and creation of large segmentation
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the reference number 27697, via our online portal: Apply now via https://jobs.uksh.de/job/Kiel-PhD-%28mfd%29-Statistical-Genetics-Machine-Learning-Schl-24105/1279933701/ For more information visit: www.uksh.de
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https://pubs.acs.org/doi/full/10.1021/acssuschemeng.5c0419 The successful candidate will be able to: Work safely and independently in a laboratory setting Learn new techniques and protocols Plan and