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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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lung and bladder barrier models (membrane-based organ-on-chip models) and for exposing those to bacteria and potential target drug compounds. A variety of analysis methods (fluorescence microscopy, mass
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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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, functional genomics, protein engineering, and targeted protein degradation. Project background High-throughput perturbation technologies rely on perturbing DNA or RNA to infer the function of proteins. Methods
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) or neural network-based methods. The level of the targeted problems will require further mathematical and algorithmic developments over the current state of data-driven SSM reduction. The PhD position will
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and innovative POM research with high relevance. We publish across engineering and management disciplines, targeting top conferences and journals. Emphasis is on top empirical research in collaboration