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methods of data analytics (e.g., statistics, stochastic analysis, Bayesian statistical analysis), physically-based hydrology and water quality models, and the use of machine learning tools for modeling flow
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work on large-scale analysis of complex traits, including Bayesian machine learning and linear mixed model approaches for trait prediction and association in high-dimensional genomic datasets, as
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statistical shape analysis, Riemannian geometry, time series and stochastic processes, and Bayesian statistics. Key responsibilities: To carry out research within the framework of the project, under
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organizational, quantitative analysis and writing skills are necessary. Candidates with a strong background in molecular virology, next-generation sequencing, Bayesian analysis, phylogenetic analysis, statistical
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learning, small data learning · Active learning, Bayesian deep learning, uncertainty quantification · Graph neural networks This position involves active participation in a well-funded
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statistical shape analysis, Riemannian geometry, time series and stochastic processes, and Bayesian statistics. Key responsibilities: To carry out research within the framework of the project, under
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patients with cancer; to identify and validate predictive biomarkers of clinical outcomes in cancer; and perform meta- analyses using the Bayesian framework. The projects will lead to both collaborative and
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methodologies. Understanding of integrating Bayesian approaches in NN-based model Knowledge of model deployments to cloud platforms or past work with AutoML tools. Knowledge of MLFlow for maintaining model
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Department of Ecology We are looking for a postdoc/researcher to develop and implement tools for analysis of output from Bayesian inference under phylogenetic models About the position A postdoc
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methods is an asset Knowledge of Bayesian statistics is an asset Excellent written and oral communication skills Strong analytical, problem-solving, and programming skills (in statistical software such as R