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Professor Valeria Vitelli. Successful candidates will work on Bayesian models for unsupervised learning when multiple data sources are available, mostly tailored to the case of dynamic sequential inference
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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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, 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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areas will be considered when selecting candidates: Machine Learning, Neural Networks, Numerical solutions of Partial Differential Equations and Stochastic Differential Equations, Numerical Optimization
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designing/programming experiments, recruiting/running participants, developing and using computational modeling approaches (Bayesian, RL, neural networks) to analyze behavioral and neuroimaging data
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computing (HPC) and parallel processing to enable the analysis of massive datasets. Experience in advanced statistical inference (e.g., Bayesian statistics, spectral methods) for extracting robust patterns
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and implement Bayesian graph neural networks and convolutional neural networks as surrogates for high-fidelity biomechanical models Quantify and propagate uncertainty, and develop strategies for model
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of the research project “Unreal engines — Understanding language models through resource-optimal analysis: Implicit Bayesian pragmatic reasoning & emergent causal world models”. The project uses
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study examining common elements in decisions across different contexts (risk, uncertainty, time; gains, losses, and mixed domain choices). Applying Bayesian techniques to develop stochastic models
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areas Biomedical applications, social determinants of health or other demographic health areas Spatial microsimulation, spatially weighted regression, combinatorial optimization or Bayesian network