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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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-makers, while actively providing the team with industry insights, contributing to successfully positioning Tethys Robotics among target customers Representing our technologically complex products in
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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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biology principles to engineer human cells and create next-generation cell-based therapies targeting cancer as well as metabolic, neurodegenerative, autoimmune and infectious diseases. Leveraging innovative
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Our group applies state-of-the-art synthetic biology principles to engineer human cells and create next-generation cell-based therapies targeting cancer as well as metabolic, neurodegenerative
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-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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using laser-induced bubble generation to achieve precise, controlled BBB opening (BBBO) for targeted drug delivery. Our key objective is to develop a non-invasive, laser-based technique that safely and
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global biodiversity targets will require not only sound ecological knowledge but also an understanding how political, institutional, and socioeconomic structures and dynamics shape conservation and