179 computational-physics "https:" "https:" "https:" "UCL" positions at ETH Zurich in Switzerland
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100%, Locarno, fixed-term Recent advances in AI-based weather prediction have demonstrated remarkable skill and computational efficiency. However, most current machine-learning weather prediction
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could have impact on diseases with inflammatory and metabolic components. Job description As a PhD student, you will: perform biological and physical characterization of the glycopolymers in different
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environmental assessment, as well as policy and regulation. Job description The doctorates will be conducted within the Einstein School’s doctoral program. The positions are for three years, starting in September
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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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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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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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to work at the interface of quantum optics, quantum information science and quantum many-body physics. Led by Prof. Wenchao Xu , the EQE group develops programmable quantum systems based on neutral atoms
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10%-30%, Zurich, fixed-term We are looking for motivated student research assistants to join our project “Adaptive Physics-Informed Neural Operators with Reduced-Order Modeling for Complex Dynamical