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- Technical University of Denmark
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- Section for Computational and RNA Biology, Department of Biology, University of Copenhagen
- University of Southern Denmark
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(e.g., Statistics, Mathematics, Physics). You will be passionate about science, hardworking, and excited to learn and improve skills in an outstanding research environment. You will be part of a
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health, though expertise in these areas is not required for applicants. The position will involve primary responsibility for data organization and statistical analysis, as well as co-authorship
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(e.g., Statistics, Mathematics, Physics). You will be passionate about science, hardworking, and excited to learn and improve skills in an outstanding research environment. You will be part of a
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September 1st, 2025, or as soon as possible. The positions are for two years with potential for extension. We seek outstanding candidates to develop generative-AI-based statistical methods for blood-based
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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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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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. • Background in experimental design in cognitive neuroscience or psychophysics. • Proficiency in programming (e.g., Python, R, C/C++, MATLAB). • Familiarity with Bayesian statistics and machine learning
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phenological data using camera and drone-based imagery. Use genetic analyses to assess the relationship between genetic differentiation and phenological variation. Develop and implement advanced statistical
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science. Your competencies PhD in computer science, statistics, data science, or a closely related field. Expertise in probabilistic machine learning, optionally generative and Bayesian models. A strong
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on developing machine-learning-based or statistical emulators to approximate key outputs of complex Earth System Models, with the aim of enabling efficient uncertainty quantification, sensitivity analysis, and