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approaches; network design and analysis; and other related topics in optimization, modeling, and decision sciences. 2. Statistics: Candidates interested in this position must have solid foundations in
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-dimensional Bayesian inverse problems for image reconstruction and chemical reaction neural networks with sparsity-promoting (and edge-preserving) priors, including diffusion-based approaches. Neural solvers
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-Nicholson Brain Institute (SNBI ) , the FAU Institute for Human Health and Disease Intervention (I-Health ) and the Institute for Sensing and Embedded Network Systems Engineering (I-SENSE ) is pleased
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public health. Is proficient in modern statistical modelling, AI & machine learning methods (e.g. system identification, regression models, Bayesian methods, deep learning). Is an experienced programmer in
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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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, e.g., by nationality (British Citizen) or 5+ years UK residency etc. Eligibility criteria and further information on the process can be found on the UK Government security vetting website, see 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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, 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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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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-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