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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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team and lead the development and application of machine learning methods to large-scale genomic data generated at IPK-Gatersleben, with a focus on the impact of genetic variation on gene regulation
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and apply cutting-edge methods in artificial intelligence (AI) to address urgent societal and environmental challenges such as improving food security, promoting animal welfare, protecting biodiversity
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(Sustainable Tourism Evolving Paradigms) will analyze possibilities for sustainable tourism in tropical island destinations. The project applies interdisciplinary mixed methods to identify and question possible
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and processing of industrial policy data and ensuring rigorous analytical methods are applied. Your Profile Required: Ph.D. in Economics Proven expertise in industrial policy research Fluent in English
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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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Analysis and other analytical methods Profile: We are looking for highly motivated candidates (m/f/d) with a PhD degree in inorganic chemistry, organometallic or organic chemistry who are interested in
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, contributing to solutions for current societal challenges with experimental methods or applied econometrics. conceiving, acquiring, managing, and working on third-party funded projects. guidance for doctoral
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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
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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