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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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. 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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the theme of borealization Communicate and interact with various stakeholder groups to ensure relevance of network activities Required selection criteria A relevant PhD degree in ecology or similar subjects
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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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candidate must have a PhD in ocean biogeochemistry, chemical oceanography, physical oceanography, ocean science, or similar field. Qualifications, skills and abilities Strong understanding of ocean
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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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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
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opportunities For more information see the LEAD AI webpage or send an email to leadai@uib.no Qualifications and personal qualities Applicants must hold a Norwegian PhD or an equivalent within psychology