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-systems-based alternative to data-based system modeling via neural networks. SSM-based modeling provides explicit and predictive polynomial ODE models for the observed dominant dynamics of nonlinear systems
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the goal to improve our understanding of the atmospheric cycling of the essential micronutrient selenium (Se) by integrating measurements and a computational atmospheric Se model. Knowing the chemical forms
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education You have several years of experience in project or program management, preferably in a research or educational environment You demonstrate strong community-building and networking skills, with
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100%, Zurich, fixed-term We invite applications for a PhD position in the field of computational neuroscience. The successful candidate will join a dynamic team at the neural dynamics lab
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Computer Science, Artificial Intelligence, or related field. Proven experience in machine learning and neural network architectures. Strong programming skills in Python and familiarity with PyTorch. Experience with
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of computational and statistical genomics, and bioinformatics. Cattle are an interesting «model organism» to study inherited genetic variation and the molecular-genetic underpinnings of complex traits and dieseases
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(IVT) at ETH Zurich intends to develop scalable optimization systems for operational support in large-scale road networks. Modeling and simulation are powerful tools for the development and validation
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systems traditionally rely on centralised control and human-in-the-loop monitoring of its moving parts. However, this requires large amounts of resources for the network operation. Operators, responsible
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interdisciplinary expertise in bioethics, philosophy, social science, medicine, public health, data science, policy and law. We pursue high-level scholarship, participate in international research and policy networks
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collaborations with other academic partners, industrial companies, and agencies – in Switzerland, Europe and worldwide Maintains an academic and industry network in Switzerland and beyond, to understand