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for health policy decision-making, these methods will be developed using a Bayesian framework. This PhD project will deliver a substantial contribution to original research in the area of health data science
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”, led by Associate Professor Valeria Vitelli. Successful candidates will work on Bayesian models for unsupervised learning when multiple data sources are available, mostly tailored to the case
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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
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will be grounded in rigorous mathematics coupled with a sound understanding of the underlying earthworm ecology. Bayesian inference methodologies will be developed to estimate where and when behavioural
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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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therapy, because of a donor organ shortage. Unfortunately, current filters cannot remove all toxins from patients’ blood, especially protein-bound uremic toxins, leading to high mortality and poor quality
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challenging, and new theoretical methods and algorithms are required. The research project aims at deriving priors for Bayesian methods from atomistic simulations and machine learning. It also offers
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expression and developability. Propose and validate optimization tools for performing (Bayesian) design of experiments. System validation and iterative refinement based on empirical data. Test and refine
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-coupled waveguides. You will determine the ideal pulsed excitation schemes for colour centres, considering spectral, temporal and polarization filtering. Subsequently, you will characterise the spin-photon
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measured data, apply necessary filtering and selection of data features to be stored. Couple the numerical model and the measured input data to establish a model that can predict the outcome in terms