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PhD students. Contributing to the teaching at the department to build your teaching portfolio for applying to academic positions. Participating actively in the research community, including attending
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statistical and machine learning techniques for dynamic energy system modelling Develop advanced optimization algorithms for building energy management and control (e.g., MPC, RL) Develop and evaluate digital
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/cognition-and-clinical-neuropsychology/ , at the Department of Psychology, University of Copenhagen. Qualification requirements The candidate must have obtained a PhD degree in mathematics, statistics
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statistical analyses for the tasks. Based on your competence and interests, your tasks will include: Develop and use epidemiological models (for example regression models or SIR-models), including for “what
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: PhD in computer science, machine learning, operations research, transportation engineering or a related field. Programming skills in C/C++ and Python, along with experience working with simulation
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Postdoc in Psychiatric Epidemiology: Linking Register and Trial Data to Study Postpartum Depressi...
closely with a PhD student and the broader project team and will contribute to the implementation and follow-up of the randomized controlled trial evaluating internet-based therapy for mothers with
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forecasting. You will get the opportunity to participate and influence the development of advanced forecast solutions combining weather forecasts and novel machine learning/statistical forecasting methods
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will collaborate closely with a PhD student and the broader project team and will contribute to the implementation and follow-up of the randomized controlled trial evaluating internet-based therapy
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Postdoc in assessing carbon sequestration potential of different wetlands as nature-based solutio...
high-impact scientific journals. Your profile The position is offered to a highly motivated candidate with a PhD degree in Soil Science, Environmental Science, Biogeochemistry, Wetland Science, Remote
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analyses to assess the relationship between genetic differentiation and phenological variation. Develop and implement advanced statistical models to quantify phenological responses. Collaborate with internal