9 bayesian-inference-"Integreat--Norwegian-Centre-for-Knowledge-driven-Machine-Learning" positions at ETH Zurich
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reliability-based design optimization and hierarchical Bayesian inversion. This specific PhD position focuses on the challenges within hierarchical Bayesian inference. Job description As the successful
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the eDIAMOND project, namely: Distributing model training and inference over a network of resource-constrained devices. Online, context-aware adaptation of Federated Neural Network Architectures based
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, electrical engineering and computer science to design highly efficient and sensitive imaging and inference approaches to help guide diagnosis and treatment in cardiovascular patients. Project background Our
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understanding of travel behavior and mobility. Our research has strong conceptual and theoretical foundations, is ideally based on primary data, uses a mix of methods, and tries to infer causality as strongly as
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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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the project will be the assessment of the model results with atmospheric measurements, as well the use of statistical inference methods for hypothesis testing. Finally the improved model will be used to provide
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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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fields Good knowledge of programming languages such as R, Python, Matlab, Mathematica or equivalent Experience in data analysis and parameter inference methods is a plus Experience in teaching and interest