173 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:" positions at ETH Zurich in Switzerland
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forces and stress fields in such systems Develop and use machine-learning based models to correlate particle deformation and contact forces in 3D systems Profile Applicants for this PhD position
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contributes to positive change in society You can expect numerous benefits , such as public transport season tickets and car sharing, a wide range of sports offered by the ASVZ , childcare and attractive
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benefits , such as public transport season tickets and car sharing, a wide range of sports offered by the ASVZ , childcare and attractive pension benefits chevron_right Working, teaching and research at ETH
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. The combination of biological and technological aspects is central in our group and in this project. A possible candidate should have strong disposition to learn and improve novel methods, should be very open to
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candidate should have strong disposition to learn and improve novel methods, should be very open to different research disciplines and should be able to communicate across disciplines. Good communication (in
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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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who share our guiding principles: Curiosity: You enjoy learning, exploring new ideas, and understanding problems deeply. Openness: You listen, collaborate, and are receptive to different perspectives
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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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to positive change in society You can expect numerous benefits , such as public transport season tickets and car sharing, a wide range of sports offered by the ASVZ , childcare and attractive pension benefits
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incorporating machine learning. 2. Transcriptome Recording and Cellular History Reconstruction We are advancing our CRISPR-based transcriptional recording method (Schmidt, Nature, 2018; Tanna, Nature Protocols