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in the research group on “Statistical models for high-dimensional and functional data ”, led by Professor Valeria Vitelli. Successful candidates will work on Bayesian models for unsupervised learning
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modelling: -Weighted PINNs, -Bayesian PINNs, -Stochastic PINNs, -Ensemble PINNs, -Domain-decomposition PINNs. Selected approaches will be tested within a dedicated data-assimilation framework
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for optimisation. (3) Machine Learning-based optimisation: implementation of a preliminary optimisation pipeline (e.g., Bayesian optimisation or reinforcement learning) integrated with the simulator to test
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-country survey datasets for comparative analysis. Conceptualize and refine a theoretical framework integrating intersectionality and stigma processes. Develop and code a Bayesian meta-regression to pool and
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intelligent feed rate optimiser. The aim is to make smarter decisions before metal is cut, not after. What you will work on The project sits at the intersection of machine learning, Bayesian inference, and
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, methodologies, and information derived from Bayesian modeling, data science, cognitive science, and risk analysis. Its primary objective is to create advanced forecasting models, generate meaningful indicators
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Discrete Mathematics Probability and Statistics Regression Analysis Time Series Analysis Bayesian Statistics Mathematical Foundations of Machine Learning Contribute to curriculum development and course
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stimulated luminescence (OSL) dating of sediments and rocks, palaeoseismology, megaliths, Bayesian chronological modelling, archaeoseismicity, stable continental regions (SCR), Armorican Massif. Context and
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at the interface of computational systems biology and mathematics/statistics with a strong attitude to open research software development. For more information visit http://www.fz-juelich.de/ibg/ibg-1/modsim
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. et al. (2024). Design of high-performance entangling logic in silicon quantum dot systems with Bayesian optimization. Scientific Reports 14, 10080. https://doi.org/10.1038/s41598-024-60478-9