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., feature engineering, spatiotemporal modeling, Bayesian calibration, ensemble methods) to improve prediction accuracy and uncertainty quantification. Disseminate research findings through presentations
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for learning about models from data, 2) incorporation of expert knowledge in model building through Bayesian prior elicitation, and 3) develop new methods for identification of conflicts in different parts
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the development and application of advanced techniques, including AutoML, Bayesian optimization, neural architecture search, reinforcement learning, and active learning, with the explicit goal of achieving
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. Probabilistic Digital Twin Synchronisation: Developing robust Bayesian frameworks and uncertainty quantification (UQ) to bridge the reality gap between real-world sensor data and high-dimensional computational
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Design Lab – works on modelling, control and optimization for mechatronic systems, industrial robots and processes (https://dynamics.ugent.be ). We are part of the department of Electromechanical, Systems
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environmental factors such as fluctuating wind speeds and saltwater exposure. Using advanced statistical and machine learning techniques, including Bayesian inference and stochastic modelling, the project will
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allow brute-force exploration it is important to select the most informative experiments. Bayesian optimization, an iterative global optimization algorithm, has been shown to be a more efficient in
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impact-based health early warning systems. The successful candidate will join the research team of Dr. Joan Ballester Claramunt (https://www.joanballester.eu/ ) at ISGlobal within the framework
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getting Bayesian type uncertainty for parameters given data (i.e., a posterior type distribution over the parameter space) without specifying a model nor a prior. Such methods can in principle be applied
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/bayesian/deep-learning analyses, with functional validation in spruce via CRISPR-Cas9 and nanoparticle delivery. The postdoc will join Professor Nathaniel R. Street’s team at UPSC, working closely with