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functional data ”, led by Associate Professor Valeria Vitelli. Successful candidates will work on Bayesian models for unsupervised learning when multiple data sources are available, mostly tailored to the case
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estimators, or machine learning) or other advanced statistical modelling. Advanced programming skills in Stata, R, Python or a similar software. Strong academic background with publications in international
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researchers for work in higher academic positions within their disciplines. Your main tasks will be Develop and apply machine learning techniques and statistical analyses, including novel methodology
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variables, fixed effects for panel data, matching estimators, or machine learning) or other advanced statistical modelling.- Advanced programming skills in Stata, R, Python or a similar software.- Strong
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science. The main purpose of the fellowship is to qualify researchers for work in higher academic positions within their disciplines. Your main tasks will be Develop and apply machine learning techniques and
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researchers for work in higher academic positions within their disciplines. The duration of appointment is 3 years. Your main tasks will be Develop and apply machine learning techniques and statistical analyses
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, computer simulations and experiments, both in fundamental and in more applied directions. The center works to advance the understanding of porous media by developing theories, principles, tools and methods
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Develop and apply machine learning techniques and statistical analyses, including digital twin methodology, to fit and validate prediction model. Perform quality control and imputation of genotype and