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Field
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to high-dimensional statistics; Bayesian statistics; resampling techniques; digital twins; uncertainty quantification; foundations of machine learning and artificial intelligence; optimization theory and
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. · Quantification and Propagation of uncertainty in industrial environments (noisy sensors, sensor degradation, evolving production processes, rare events, incomplete datasets…) o quantifying epistemic and
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, recurrent memory, Bayesian modelling, uncertainty quantification and machine learning systems. Emphasis will be on methods that design and implement new architectures for (auto-regressive) sequence modelling
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), functional genomics, or comparative genomics at scale. Experience with model calibration, uncertainty quantification, or active learning. Experience with genome-scale metabolic modeling or pathway analysis
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estimation of 3D coronary hemodynamics (velocity, pressure, and wall shear stress fields); design computational pipelines that integrate image-based anatomy, blood flow physics, and uncertainty quantification
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-the-loop, decision-making for complex systems, optimisation for LLMs, foundation models, dimensionality reduction, deep learning, uncertainty quantification, language, and developmental robotics. About You
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-time data acquisition and telemetry systems Familiarity with cloud computing platforms and edge deployment of ML models Experience with uncertainty quantification, sensitivity analysis, or robust
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, formal verification, proof assistants, automated proof search, and AI-guided mathematical discovery; neural operators, physics-informed learning, inverse problems, uncertainty quantification, and rigorous
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states are unobserved. Purely data-driven models offer flexibility, but often ignore known biology and provide limited insight into uncertainty and mechanisms. These challenges motivate a broader Biology
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relevance, and who are interested in developing novel approaches for uncertainty quantification, large-scale prediction, model-based learning, causal inference, network learning, or related areas. Experience