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the analysis of massive datasets. Extensive experience in signal processing and advanced statistical inference (e.g., Bayesian statistics, Fourier analysis) for extracting robust patterns from noisy, high
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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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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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experimental methods. Develop and apply methods for demultiplexing, normalization/QC, effect-size estimation, biological inference, and predictive modeling. Contribute to biological manuscripts and methods