189 machine-learning-"https:" "https:" "https:" "https:" "U.S" uni jobs at ETH Zurich
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COMPAS XR framework developed at ETH Zürich. Project background The successful candidate will work at the intersection of computational design, XR, human-computer interaction, and robotic fabrication, with
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knowledge and technology from research to Swiss machine, electrical and metal industries. The research group Laser Material Processing at inspire offers in collaboration with the Advanced Manufacturing
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, Computer Vision, Control Systems, Deep Learning, Digital Humans, Earth Observation, Educational Technology, Efficient AI, Explainable AI, Graph Representation Learning, Haptics, Human-Computer Interaction
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the power of both classical and quantum computing resources? How can we exploit or take inspiration from quantum physics to develop cutting-edge machine learning? Your work will encompass a diverse array of
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at https://ethz.ch/en/the-eth-zurich/working-teaching-and-research/faculty/faculty-affairs/ausgeschriebene-professuren/naturwissenschaften-und-mathematik/APTT_Anorg_Chem.html . Contact: faculty-recruiting
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Master’s degree in Computer Science, AI, Machine Learning, Mathematics, Electrical Engineering, or a closely related field; or Master’s degree in Medicine (MD) with strong Python skills and some ML
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100%, Zurich, fixed-term Human–Computer Interaction in Architecture and Digital Fabrication This fully funded, full-time PhD position spans four years and is embedded within the interdisciplinary
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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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methods. Contributing to the development, adaptation, and application of machine‑learning models tailored to RODI data (in collaboration with project partners). Designing and implementing an innovative
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understanding of numerical weather prediction and meteorological applications Experience with, or strong interest in, machine learning, ranging from classical methods (e.g. random forests) to modern deep learning