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) for engineering systems. Our research covers surrogate modeling, reliability analysis, sensitivity analysis, optimization under uncertainty, and Bayesian calibration. We are known for developing the UQLab software
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the admission requirements for a PhD at ETH Zurich Experience in machine learning, optimization, or AI-driven decision-making Preferably with knowledge of Bayesian optimization or Gaussian processes
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) for engineering systems. Our research covers surrogate modeling, reliability analysis, sensitivity analysis, optimization under uncertainty, and Bayesian calibration. We are known for developing the UQLab software
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recovery trajectories and injury patterns. Integrate personalized physiological measurements into a recovery prediction model, while adapting Bayesian Neural Networks for SCI data and analyzing the impact on
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), Multi-armed Bandits, Bayesian Optimization. Automated Model Design and Tuning: Neural Architecture Search, Hyperparameter Optimization. Computer Networking: Resource-Constrained Networking (e.g., Internet
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multiphase fluid phenomena, such as bubble and droplet dynamics and the resulting fast flows. One of our key objectives is to control bubble oscillations to exploit their energy-focusing characteristics in
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department (hr@inspire.ch ), including the following material: A short statement of research interests and objectives A CV including past research work and projects 2-3 reference letters/contacts One
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exposure, pesticides and climate change including heat waves. Internship or MSc Project (80-100%) for 4-6 months The overall objective of this internship is to contribute to the development of a Swiss 5G
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the fight against climate change. Policymakers need new tools to help balance the transition’s big-picture objectives with the systems’ impacts on people and communities. This project will contribute new
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defiance by centering on visual analysis grounds aspirational urbanism, rescaling the object of urban theory towards everyday micro-practices.