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modeling, differential equations, Bayesian inference, large-scale computational methods, bioinformatics, data science, machine learning, optimisation, numerical methods. Please read more about the position
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of Applied Mathematics and Statistics we conduct research within the theory and implementation of biomathematics, biostatistics, spatial modeling, differential equations, Bayesian inference, large-scale
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of biomathematics, biostatistics, spatial modeling, differential equations, Bayesian inference, large-scale computational methods, bioinformatics, data science, machine learning, optimisation, numerical methods
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of Applied Mathematics and Statistics we conduct research within the theory and implementation of biomathematics, biostatistics, spatial modeling, differential equations, Bayesian inference, large-scale
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Mathematics and Statistics we conduct research within the theory and implementation of biomathematics, biostatistics, spatial modeling, differential equations, Bayesian inference, large-scale computational
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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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version control and containerization (Docker/Singularity) Statistical Modeling: Quantitative data analysis using GLMs, Bayesian methods, or mixed-effect models to interpret complex perturbation datasets