53 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" "NORTHUMBRIA UNIVERSITY" PhD positions in Switzerland
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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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at the interface of machine learning, statistics, and live-cell biology. The position is co-supervised by Prof. Olivier Pertz (Cell Biology) and Prof. David Ginsbourger (Statistics), and the student will be equally
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dynamics simulations is highly desirable. Basic knowledge of machine learning is considered an advantage but is not mandatory. LanguagesENGLISHLevelExcellent Additional Information Work Location(s) Number
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and nurture a working and learning environment in which the rights and dignity of all our staff and students are respected. Visit our Equal Opportunities and Diversity website to find out how we ensure
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environment. In line with our and Uni Basel values ( https://www.unibas.ch/en/Research/Values-Ethics/Diversity-and-Inclusion.html ), we are committed to sustain and promote an inclusive culture, ensure equal
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with competitive salary according to ETH standards Interdisciplinary and international research environment You can expect numerous benefits , such as public transport season tickets and car sharing, a
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. The deadline for applications for this Fall Call is April 20, 2026. Please submit your application to our PhD program or our MD-PhD program online. Where to apply Website https://www.fmi.ch/education-careers
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Opportunities to learn cutting edge techniques Perspectives for career development A diverse and interdisciplinary team Working, teaching and research at ETH Zurich We value diversity and sustainabilityIn line
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environment. n line with our and Uni Basel values (https://www.unibas.ch/en/Research/Values-Ethics/Diversity.html ), we are committed to sustain and promote an inclusive culture, ensure equal opportunities and
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systems, and space applications. We combine theory, physics-based simulations, machine learning, and autonomous workflows to understand and design materials that can perform under conditions where