385 computational-physics "https:" "https:" "https:" "https:" "Tilburg University" positions in Switzerland
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datasets The position is limited to two years. Profile University degree (MSc or PhD) in data science, computer science, physics or a related field Experience in training and validating large-scale deep
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years. Profile University degree (MSc or PhD) in data science, computer science, physics or a related field Experience in training and validating large-scale deep-learning models on distributed systems
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operations that are yet to be fully understood. In this context, it is evident that the operation, control, and planning of power systems will soon be pushed to their limits. Therefore, new computational
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–2028 cycle will open in November 2026. Help Us Improve Our Fellowship Application Process We noticed that some applicants began the application process but did not submit their application
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100%, Basel, fixed-term A World-Class Research Environment at the Nexus of Biology, Engineering, and Physical Sciences The Biotechnology and Bioengineering group led by Prof. Dr. Martin Fussenegger
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100%, Basel, fixed-term A World-Class Research Environment at the Nexus of Biology, Engineering, and Physical Sciences The Biotechnology and Bioengineering group led by Prof. Dr. Martin Fussenegger
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, statistical physics and quantum solid state physics is absolutely essential. Good computer skills are a plus. For further information see our group web page www.neutron.ethz.ch/. Applications should contain a
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computational workflows to design novel AAV capsids Dry-to-wet: lead the computational design process and actively participate in wet lab validation of your designs (e.g., library construction, viral production
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have enabled unprecedented control over light-matter interactions, catalyzing breakthroughs in imaging, nonlinear optics, and photonic computing. We leverage these developments to advance the field
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models Lead the design and implementation of innovative methods, which could include but are not limited to: Kriging surrogate, Polynomial Chaos Expansion (PCE), and Physics-Informed Neural Networks (PINNs