338 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" "University of Waterloo" 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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the Job related to staff position within a Research Infrastructure? No Offer Description Postdoc in Machine Learned Semiconductor Material Properties for Quantum Transport Simulations The simulation
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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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Scientist / Scientific assistant – Tree species detection using deep-learning Are you an ambitious data scientist with strong analytical and numerical skills, and expertise in geomatics, remote sensing, and
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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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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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a maximum of four years • Deadline: 15 December 2025 We are not accepting applications for this job through MathJobs.Org right now. Please apply at https://careers.epfl.ch/job/Lausanne-Postdoctoral
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better learning and reasoning over data. We are looking for a Postdoctoral Researcher for an initial project of 24 months (extendable depending on funds). In partnership with a leading company in the field
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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
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combining advances in Physics-Informed Machine Learning (PIML) and Graph Neural Networks (GNNs) with real-world energy applications, the project aims to better capture the dynamics of urban infrastructures