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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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upcoming areas off the beaten paths. Our three main areas of research are machine learning, distributed systems, and theory of networks. Within these three areas, we are currently working on several projects
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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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, 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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Computational Design Lab and work at the interface of computer vision, computer graphics, hardware, and extended reality. The project is part of ETHAR, a new research initiative at ETH Zürich with a unique focus
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modeling of historical controls, as well as machine learning, data science, and epidemiological studies based on large SCI datasets. This is an excellent opportunity to contribute to translational research
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scientists, lab technicians, machine learning engineers, and external partners at the interface of automation, software, and experimental catalysis. The position is initially offered as a fixed-term contract
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we seek to enhance resources for student learning on statistics and machine learning applied to these topics. Project background We would like to develop learning exercises that help students learn how
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of computer graphics fundamentals, numerical methods, and GPU/parallel computing concepts. Experience with at least one major deep learning framework (PyTorch preferred). Excellent problem-solving skills and
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discovery, and machine learning. In the wake of quantum mechanics' initial breakthroughs, we're on the brink of a second quantum revolution. Quantum physicists are adopting machine learning to explore complex