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learning Your competencies Prospective applicants to this PhD proposal should have the following qualifications: For three-year stipends: MSc degree in electrical engineering, communications engineering
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algorithms. Graph Neural Networks. The candidate is expected to hold a relevant MSc degree in Computer Science, Data Science, Physics, (Applied) Mathematics, Computational Statistics or another field
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at Aalto University (https://into.aalto.fi/display/endoctoralsci/How+to+apply#Howtoapply-Eli… ) a Master’s degree in Artificial Intelligence, Machine Learning, Computer Science, Cognitive Science
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Are you fascinated by understanding fundamental neurobiological processes in the context of stress and depression? Are you intrigued to learn more about early-life stress as risk factor for
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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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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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or for multiobjective optimization problems. Implement the developed algorithms (e.g., in Python) and evaluate their practical performance on artificial and/or real-world data. Teach tutorials (in English) for
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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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organizing collective behaviour Analysing interspecific variation in swarming behaviour using comparative and statistical approaches Exploring evolutionary hypotheses by clustering behavioural traits and
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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