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-informed / simulation-aware modeling Efficient algorithms for design-space exploration (e.g., surrogate modeling, Bayesian optimization, differentiable programming) Hybrid approaches combining data-driven
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of error-controlled biomechanical models in SOFA / FEniCSx / SOniCS for real-time use on AR devices Design of Bayesian neural-network surrogates and graph-based models for tissue deformation and brain shift
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. Is proficient in modern statistical modelling, AI & machine learning methods (e.g. system identification, regression models, Bayesian methods, deep learning). Is an experienced programmer in R and/or
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analysis, Bayesian Skyline Plots, PCA, Bayescan - information provided in the CV and/or in the motivation letter; Other professional experience: teaching activities in evolutionary biology and phylogenetics
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experience (front-/back-end, metrology/inspection, equipment maintenance, yield-improvement projects). Digital twin, IIoT, MLOps, real-time data streaming, edge computing. Causal/XAI, Bayesian methods
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, machine learning, deep (reinforcement) learning, Bayesian optimisation, control theory, dynamical system theory and/or robotics. Experience with hardware development is desirable but not mandatory. You have