206 assistant-professor-computer-science-data-"https:"-"https:"-"https:"-"https:" positions at ETH Zurich
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of mathematics, computer science, and evolutionary biology. We develop methods to understand evolutionary, ecological, epidemiological, and developmental processes on different scales based on genetic data. In our
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challenges. The work is conducted at the interface of mechanics, artificial intelligence, and computational science. The developed methods will be validated on benchmark problems and real-world data and
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psychologists, neuroscientists, computer scientists, clinicians, and data scientists across the Singapore-ETH Centre (SEC), the National University of Singapore (NUS), and Nanyang Technological University (NTU
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, neuroscientists, computer scientists, clinicians, and data scientists across the Singapore-ETH Centre (SEC), the National University of Singapore (NUS), and Nanyang Technological University (NTU), the PhD student
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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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acceleration Scientific computing and data libraries A keen interest in scientific computing, atmospheric sciences, or advanced instrumentation is highly advantageous Ability to work both independently and
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, with deep experience in appearance reconstruction and material modeling. Required Qualifications MSc in Computer Science or related field Strong background in computer vision and/or computer graphics
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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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100%, Basel, fixed-term The Computational Biology (CoBi) group, led by Prof. Dagmar Iber, develops data-driven, mechanistic models of biological systems using advanced imaging and computational
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Engineering, Ecology, Ecohydraulics or a related discipline. Demonstrated experience in planning and conducting fieldwork in freshwater ecosystems. Strong skills in data management, visualisation and