160 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "Simons Foundation" positions at Nature Careers in Germany
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The Faculty of Engineering at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) invites applications for an Assistant Professor of Machine Learning in Digital Health (salary group W1
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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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theoretical methods and algorithms are required. The research project aims at deriving priors for Bayesian methods from atomistic simulations and machine learning. It also offers the opportunity to work with
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the Interreg project webpage ( https://www.sn-cz2027.eu/de/projekte/prioritat-2-klimawandel-und-nachhaltigkeit/100781629_beech ). For TUD diversity is an essential feature and a quality criterion of an excellent
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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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well as basic research with the combined tools of immunology, microbiology, virology, cell biology and molecular biology. For more information, please see https://www.mhh.de/hbrs/zib MD/PhD Molecular Medicine:The
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postdoctoral research track record and are expected to build and lead a group to pursue a high-quality research programme. Current research interests within the Division of Condensed Matter Theory (https
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: https://uni-tuebingen.de/en/213700 Please submit your entire application using the web-based application platform https://berufungen.uni-tuebingen.de. The deadline for applications is 26 February 2026
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arrival date of the university central mail service or the time stamp on the email server of TUD applies), preferably via the TUD SecureMail Portal https://securemail.tu-dresden.de by sending it as a
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of Medical Image Computing (MIC) is a leading research group pioneering advancements in machine learning and information processing to improve cancer patient care through systematic image data analytics