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used will be Density Functional Theory, statistics, machine-learning and dynamics. Collaboration with members of other research groups at UCPH and abroad is required. Who are we looking for? We
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Solid experience with statistical modeling, machine learning, or AI Practical skills in R and/or Python for data analysis and model development Familiarity with microbial ecology, genomics, or food safety
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community, there is a committed and passionate atmosphere. The themes of Health and Well-being, Social-Cognitive-Affective Decision Making, Development and Learning, and Advanced Behavioral Research Methods
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the institute and the academic community, there is a committed and passionate atmosphere. The themes of Health and Well-being, Social-Cognitive-Affective Decision Making, Development and Learning, and Advanced
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and presenting research ideas and results. Education & work experience · Basic: Msc. in environmental sciences, ecological economics or environmental engineering. · Education or proven
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the BSc and MSc programs of the ENR Group; and actively contributing to a dynamic, inclusive, and collaborative research culture within the research group. You will work here The research is embedded within
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machine learning methods to investigate how ecosystem water stress and drought disturbances affect relevant forest ecosystem functioning at various scales. It will enable advanced assessment of forest
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requirements The candidate should have an MSc degree (or equivalent) in one of the following fields: • (Marine) Biology • (Marine) Ecology • Marine Sciences • or a related discipline Profound knowledge and hands
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comparative and statistical approaches Exploring evolutionary hypotheses by clustering behavioural traits and mapping them onto phylogenetic trees Collaborating with a multidisciplinary team of biomechanists
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organizing collective behaviour Analysing interspecific variation in swarming behaviour using comparative and statistical approaches Exploring evolutionary hypotheses by clustering behavioural traits and