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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
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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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, 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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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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PhD degree in ecology or similar subjects Relevant background in research in ecology or related fields of research Excellent communication skills both written and oral English The appointment is to be
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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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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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. Applicants must have submitted their PhD thesis to the PhD evaluation committee when sending the application. Appointment is conditional on having successfully defended the PhD thesis. The candidate´s PhD
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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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Qualifications and personal qualities Applicants must hold a Norwegian PhD or an equivalent degree within law, or must have submitted his/her doctoral thesis for assessment prior to the application deadline. It is