218 assistant-professor-computer-science-"https:"-"https:"-"https:"-"https:"-"UCL" positions at ETH Zurich
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evaluate prototypes together with industrial partners Profile Required experience CH/EU/EFTA citizenship or valid Swiss work permit PhD in Engineering, Computer Science, Robotics, or related field Strong
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researcher to advance our understanding of magmatic and volcanic systems. Job description Develop and lead an independent research program in magmatic petrology, volcanology, and/or geochronology. Publish
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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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scientometrics Profile Background Master’s degree ideally in information science, library science, data science, computer science, or a comparable field; a PhD is an advantage Regardless of academic background
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, Computer Science, Applied Mathematics, or a field focused on the intersection of Machine Learning and Optimization Proven expertise in surrogate modelling, specifically in designing neural architectures
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80%-100%, Zurich, fixed-term The Swiss National Supercomputing Centre (CSCS) develops and operates a high-performance computing and data research infrastructure that supports world-class science in
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researchers across architecture, robotics, computer science, and engineering An engaged international community of researchers and educators Professional development and networking opportunities through
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March 23rd for a Zoom interview, as well as your desired start date. This is to help us facilitate logistics and scheduling for the Zoom interviews, and will not affect candidate evaluation. Contact
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Each doctoral student will lead a coherent subproject within this broader research program: Position 1: AI for Computational Thinking Focuses on designing and studying AI-assisted programming
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of Medical Microbiology at the University of Zurich, the Department of Informatics at ETHZ and several further partners, we address the challenge by the combining microfluidic technology, sequencing and fast