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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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Your profile We seek candidates with an outstanding research record in deep learning, in particular in one or several of the following areas: modeling and architecture development, domain adaptation
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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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. • Familiarity with machine learning, dimensionality reduction, clustering, and statistical modeling. • Strong communication skills, interest in interdisciplinary work, and ability to train students and postdocs.
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experience working in collaboration with biological or clinical labs and with groups with a strong machine learning background. The starting date is by mutual agreement. We expect a pronounced interest in
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of machine learning and high-performance computing, tackling complex, open-ended challenges to deliver scalable solutions. You will design and optimize a software-defined infrastructure that enables cutting
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library. Strong interest in machine learning, reinforcement learning, and fluid dynamics. Ability to work independently and collaboratively in an interdisciplinary team. Excellent command of English, both
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the environmental drivers that regulate these processes. We will use machine learning approaches (XGBoost, SHAP analyses) for the flux partitioning, complemented by existing tree dendrometer and sap flow measurements