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. Compare advanced deep learning–based methods with probabilistic approaches. Collaborate with researchers at Chalmers, the University of Gothenburg, and international experts in Bayesian inference and
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. Collaborate with researchers at Chalmers, the University of Gothenburg, and international experts in Bayesian inference and optimal control. Present your results at international conferences and publish in
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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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version control and containerization (Docker/Singularity) Statistical Modeling: Quantitative data analysis using GLMs, Bayesian methods, or mixed-effect models to interpret complex perturbation datasets