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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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: Experience in machine learning, optimization, or AI-driven decision-making Preferably knowledge of Bayesian optimization or Gaussian processes Programming experience (Python, MATLAB, or similar) Soft Skills
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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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10%-30%, Zurich, fixed-term The Centre for Energy Policy and Economics (CEPE) at ETH Zurich is seeking a student research assistant to work on the project ‘Sustainable Consumption’. The objective
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defiance by centering on visual analysis grounds aspirational urbanism, rescaling the object of urban theory towards everyday micro-practices.
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. A nontechnical introduction can be found under here , whereas a more technical introdution is available from here . Job description The objective of the position is to work within a Swiss-French
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documents: CV short statement of objectives and research interests (1-2 pages) transcripts of all degrees obtained (in English) names and contact information of three references one publication (e.g. thesis