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and policy. Phase 2: Comparative Analysis - Apply multiple analytical perspectives to critically examine the collected materials. Phase 3: Model & Method Development - Map findings onto established
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algorithms and methods for calibrated Bayesian federated learning for trustworthy collaborative Bayesian learning on data from multiple participants. The project will develop new methods, theory, and
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multiple scales, while Multiobjective optimization strategies ensure improvements in efficiency across various biomanufacturing scales. Expected results: 1) Development of innovative bio-based production
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classification Estimation of the probability of developing diseases Anomaly detection Early diagnosis. The recipient will learn and apply a vast portfolio of complementary and synergic methods at the intersection
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aimed at developing novel Artificial Intelligence–based methods and software to assist physicians with: Disease classification Treatment decision support What-if analyses. Although the developed methods
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to apply mathematical modelling methods to study adrenal gland steroid biosynthesis dynamics and their spatial relationship with adrenal tumours found in Primary aldosteronism (PA) and Mild autonomous
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methods to estimate food passage do not measure food directly, are impractical for many species, and often require unnatural conditions to administer. This new method directly measures the transit and
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training network and aims to apply mathematical modelling methods to study adrenal gland steroid biosynthesis dynamics and their spatial relationship with adrenal tumours found in Primary aldosteronism (PA
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pseudorange correction through multiobjective optimisation. The research will explore multiple classes of constraints that will be embedded as objectives: Internal pseudorange consistency: ensuring
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- Fundamental of computational fluid dynamics, and experience with CFD software - Methods for design & optimisation - Computer assisted design and prototyping, - Experience with