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addressing specific case studies or specific targeted techniques. The main tools to be used will come from the discipline of Machine Learning, particularly those based on Bayesian methods. The student will be
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system reliability and maintenance strategies. Filter Rig: An experimental setup to study filter clogging phenomena, allowing for the collection of data to develop and validate prognostic models for filter
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reliability and maintenance strategies. Filter Rig: An experimental setup to study filter clogging phenomena, allowing for the collection of data to develop and validate prognostic models for filter
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, supporting studies in electronic system reliability and maintenance strategies. Filter Rig: An experimental setup to study filter clogging phenomena, allowing for the collection of data to develop and validate
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Bayesian methods, deep learning, deep generative models, reinforcement learning, graph neural networks. Interviews are expected to happen in July 2025. Applicants are encouraged to guarantee that referees
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Bayesian methods, deep learning, deep generative models, reinforcement learning, graph neural networks. Interviews are expected to happen in July 2025. Applicants are encouraged to guarantee that referees
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useful life of electronic components, supporting studies in electronic system reliability and maintenance strategies. Filter Rig: An experimental setup to study filter clogging phenomena, allowing
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the remaining useful life of electronic components, supporting studies in electronic system reliability and maintenance strategies. Filter Rig: An experimental setup to study filter clogging phenomena, allowing
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modern Bayesian modelling frameworks such as Stan, Turing.jl, and PyMC, including automatic differentiation frameworks, MCMC sampling algorithms, and iterative Bayesian modelling. Special attention will be
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interactions. Safety Layer: Introduce a supervisory “filter” based on control-barrier functions that provably enforces state constraints (e.g. collision avoidance, bounded inputs) without destroying