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placed on deriving velocity and flow depth estimates at unprecedented spatial and temporal resolutions by analyzing point cloud and camera data You will develop a deep understanding of debris flow
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calculations will be performed using ETH Zurich’s high-performance computing infrastructure. In addition to research, the doctoral candidate will contribute to general laboratory activities and will have
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, material science, computational science, or a related discipline You have a background in computational mechanics, solid mechanics, applied physics, scientific computing, or a related field You have
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an emphasis on object tracking, optical flow and sensor fusion Knowledge of rock mechanics, soil mechanics and/or landslide processes is considered beneficial Prior experience with point cloud
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teaching assistantships, as well as access to state-of-the-art computing infrastructure, laboratories, and research equipment. Working, teaching and research at ETH Zurich We value diversity and
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point cloud processing, as well as in probabilistic modelling and Bayesian inference Very good programming skills, preferably in Python Familiarity with digital twins, building information modelling (BIM
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computing, and could provide a physical route to Ising and Boltzmann machines that operate on dynamical rather than static states. Job description Build and operate a programmable network of coupled