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data collection approaches. Familiarity with or strong motivation to learn machine learning or advanced data analytics for pattern detection and forecasting in environmental data. Familiarity with
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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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the SFF Integreat, The Norwegian Centre for Knowledge-driven Machine Learning (ML) , a centre of excellence funded by RCN and in operation until 2033. The project PI and team are also in close collaboration
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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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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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. Your main tasks will be Develop and apply machine learning techniques and statistical analyses, including novel methodology for analysis of complex polygenic traits and prediction tools for precision
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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
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studies. Proficiency in relevant computational tools and statistical methods. Experience with machine learning in large datasets. Interest and motivation to work in a multidisciplinary team. Ability to work