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Field
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, for the development and operation of space missions. LUX benefits from an extensive international network of partner institutions through its participation in major projects such as ALMA, SKA, ELT, HESS, CTA, SVOM
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. Experience with Bayesian methods, graph/network analytics, reinforcement learning, or other advanced AI approaches relevant to industrial systems. Experience with geospatial analysis, spatial data integration
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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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. The candidate shall take part 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
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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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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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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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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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areas Biomedical applications, social determinants of health or other demographic health areas Spatial microsimulation, spatially weighted regression, combinatorial optimization or Bayesian network