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missing factor in soil GHG flux models. BoTiKI aims at filling this knowledge gap and establish improved GHG models accounting for soil fauna. To achieve this, we create a rich AI-training dataset
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Expertise with scientific data analysis We expect: the ability to work independently and on your own initiative a methodical and systematic approach structured and goal-oriented thinking capacity
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experience through articles in internationally recognized refereed journals or working papers suitable for publication. You contribute in-depth knowledge of methods of applied microeconomics, experimental
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Office, Linux proficient in dealing with operating systems and high-performance computing facilities We expect: the ability to work independently and on your own initiative a methodical and systematic
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to identify transcription-factor master regulators and derive targets for gene editing / TILLING. You integrate multi-omics data to identify genotype-phenotype associations. You ensure FAIR data management
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/ research stays, and/or presentations at international conferences; Proven experience in quantitative and/or qualitative social-science methods; Willingness and ability to further PRIF’s research agenda
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or Python Machine learning methods (for the baseline prediction for the reward funds) is beneficial We expect: Strong motivation to contribute to policy-relevant research Strong interest in teamwork and
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) and programming languages (e.g. Python, Matlab, R) as well as in advanced statistical methods for analyzing complex ecosystem and environmental datasets. Good knowledge of European marine ecosystems as
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the team of Prof. Dr. Loriana Pelizzon on data and methods for supervision of climate and ESG risks in Capital Markets (incl. investment funds, bonds, stocks). The role involves advising and
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research questions a strong collaborative spirit and enjoyment working closely within a diverse research team intellectual curiosity, creativity, and an openness to exploring new methods and