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sciences (e.g., meteorology, climatology), or a related field; Strong programming skills; Data analysis and interpretation experience; A good team spirit and willingness to collaborate; Very good command
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OMICs-data handling and statistical analysis Documented programming skills and excellent R user. Previous experience from scientific data generation, analysis and publishing Hands on skills in
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Publications or basic experience in scientific work and research methods Basic skills in scientific programming Knowledge of academic processes and structures Experience abroad (research, study, or both
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skills in data analysis and programming (R-applications or comparable programming languages) using large datasets Determination and commitment to scientific excellence Willingness and ability to drive
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successful candidate will have Proven knowledge of soil science, fire ecology, and reed ecology Proven knowledge of data analysis and programming (R applications or comparable programming languages
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mathematical optimization and quantitative modelling. Experience with optimization under uncertainty or data-driven decision-support methods is highly desirable. Programming experience in optimization
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continuing education and training on digitalization topics such as programming, research data management, AI applications, and machine computing in science. Networking and exchange in our
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master’s degree or equivalent within Computer Science, Operations Research or Applied Mathematics. Qualifications within the following areas are required Linear and Integer programming Machine learning
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qualifications: Honours/Msc degree in microbiology/bioinformatics, with specific emphasis on functional microbial multi-omic data analysis. Knowledge of a programming language e.g. Python/R. Desired
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Engineering Process Engineering Or a closely related discipline A background in thermodynamics, energy systems, process modelling, programming, or simulation tools such as MATLAB, Python, Aspen Plus