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- University of Amsterdam (UvA); Published yesterday
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theories from tractable models (probabilistic circuits) and Bayesian statistics to tackle the reliability of machine learning models, touching topics such as uncertainty quantification in large-scale models
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measurements are most informative and guiding where, when and how to observe next. By combining Bayesian inference, probabilistic modeling, and machine learning, the project aims to make Arctic observations more
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features to behavior using GLMMs/Bayesian models; conduct sensitivity and robustness checks. * Method validation: benchmark alternative pipelines (filters, burst detectors, forward/inverse models); perform
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has a strong background in control engineering, with documented expertise in optimal control, adaptive control and online optimization, stochastic systems, Bayesian inference, and state estimation and
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communication and collaboration skills Preferred: Experience with simulation-based inference and Bayesian methods Familiarity with cosmological simulations or observational cosmology ML architecture design and
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experimental methods. Develop and apply methods for demultiplexing, normalization/QC, effect-size estimation, biological inference, and predictive modeling. Contribute to biological manuscripts and methods
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areas Biomedical applications, social determinants of health or other demographic health areas Spatial microsimulation, spatially weighted regression, combinatorial optimization or Bayesian network
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-portals and visualizations for complex data Building models and sensors for the estimation of ecosystem carbon exchange in urban and forested environments. Candidates will be expected to contribute