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integration, especially in molecular medicine; mathematical modelling of cancer; probabilistic modelling and Bayesian inference, stochastic algorithms and simulation-based inference; causal inference and time
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of cancer; probabilistic modelling and Bayesian inference, stochastic algorithms and simulation-based inference; causal inference and time-to-event analysis; and statistical machine learning in general. OCBE
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page to watch video, or click here to open video) About the position Embankment Dam Safety under Flexible Hydropower Operation: Internal Erosion and Filter Performance About the position The Department
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, linear algebra, probability theory, (Bayesian) statistics, optimization and elementary graph theory Familiar with machine learning and deep learning Programming experience (Python or Julia) and their
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will consider techniques like flow matching, and use ideas from optimal transport and neural (stochastic) differential equations, invariant Kalman filtering and geometric numerical integration
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transport and neural (stochastic) differential equations, invariant Kalman filtering and geometric numerical integration. The application areas will be chosen among the use cases of the aiD canter
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criteria Machine Learning Expertise: A robust foundation in probabilistic modeling, Bayesian inference, deep learning, and/or anomaly detection Modeling & Simulation Experience: Familiarity with Building