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on hormonal time series data collected at unprecedented time resolution in healthy humans and in patients, including studies in real life settings with a state-of-the-art wearable device (https
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on hormonal time series data collected at unprecedented time resolution in healthy humans and in patients, including studies in real life settings with a state-of-the-art wearable device (https
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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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, including Tikhonov regularization [3], Bayesian approaches [4], and compressive sensing or sparse regularization methods [5]. However, with the emergence of Physics-Informed Neural Networks (PINNs), new
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/Master’s degree in statistics, mathematics, computer sciences or a related field Thorough knowledge of methods in event history analysis and multi-state models is required. The candidate should be familiar
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as possible and can handle the constraints. • Integrate them into Bayesian models to sample the posterior distribution and provide uncertainty quantification on the estimated parameters. • Generalize
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-informed machine learning. The ideal candidate will have a strong background in developing and integrating probabilistic graphical models, Bayesian networks, causal inference, Markov random fields, hidden
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LLM-based scientific agents. In the project, you will (i) identify potential sources of uncertainties in AI agents, (ii) investigate ways to assess the quality of uncertainty estimates by standard
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computer code. Preferred Qualifications PhD in ecology, evolution, or closely related field. Expertise in one or more of the following biodiversity groups: fish, birds, reptiles, amphibians, insects