226 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:" positions in Switzerland
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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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1855, ETH Zurich today has more than 18,500 students from over 110 countries, including 4,000 doctoral students. About 500 professors currently teach and conduct research in engineering, architecture
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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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dynamical systems, and machine learning, with applications to synthetic biology and biomolecular circuit design. Our research develops mathematical and computational frameworks for understanding and
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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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-read sequencing data analysis is highly desirable. Familiarity with signal processing or applied machine learning is advantageous. You should demonstrate strong motivation to develop innovative
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of this programme. The profile PhD in computer vision, computational biology, physics or a related discipline Demonstrated expertise in image analysis and working with large-scale imaging datasets Strong expertise 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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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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bioelectronics or software simulations, or a strong willingness to acquire it, alongside solid knowledge of electrochemistry and materials chemistry Candidates should be eager to collaborate closely with molecular