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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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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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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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. As part of the project, you will be involved in Conducting original research in probabilistic machine learning, within the topics mentioned above, focusing on generative and Bayesian models relying
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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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. Experience leading investigations linking simulations to observational data. Experience with statistical characterization of data, preferably within a Bayesian framework. Job Description: A Post-doctoral
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clinical trials in 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
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to galaxy evolution and observations of galactic magnetic fields. Experience with Bayesian statistics is advantageous. Strong verbal and written communication skills in English are required. To qualify
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linear ballistic accumulator models, diffusion models, biased competition models, or Bayesian models. During the employment, the candidate is expected to engage in the development of computational models
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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 well as methods