350 machine-learning-"https:" "https:" "https:" "https:" "https:" "UCL" "UCL" "UCL" "UCL" positions in Switzerland
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- ETH Zurich
- University of Basel
- ETH Zürich
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- Paul Scherrer Institut Villigen
- Swiss Federal Institute for Forest, Snow and Landscape Research WSL
- EPFL - Ecole Polytechnique Fédérale de Lausanne
- HES-SO Genève
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- Friedrich Miescher Institute for Biomedical Research
- Graduate Institute of International and Development Studies, Geneva;
- Idiap Research Institute
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- Physikalisch-Meteorologisches Observatorium Davos (PMOD)
- School of Architecture, Civil and Environmental Engineering ENAC, EPFL
- University of Geneva
- Università della Svizzera italiana (USI)
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knowledge and technology from research to Swiss machine, electrical and metal industries. The research group Control and Automation at inspire AG offers the following position in collaboration with Bota
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the use of hierarchical graph neural networks for modeling multi-scale urban energy systems. By combining advances in Physics-Informed Machine Learning (PIML) and Graph Neural Networks (GNNs) with real
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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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thinking with a structured, quality-focused approach to data and methods. Ideally, experience in one or more of the following: data engineering, building data-driven apps, computational linguistics, machine
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Computer Vision and Computer Graphics techniques to digitize human avatars and garments in 3D. Within this project, your role is to advance our existing algorithms that reconstruct 3D garments from multi
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of machine learning, AI, and cancer genomics. Our lab develops novel machine learning methods to understand biological systems and cancer, with a strong focus on genomics and translational impact. We work in
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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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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
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not only supports your professional development, but also actively contributes to positive change in society You can expect numerous benefits , such as public transport season tickets and car sharing, a
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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