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. Advance Bayesian and ensemble learning approaches for non-stationary temporal processes. Implement probabilistic diffusion or generative models for long-term forecasting. Collaborate closely with
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uncertainty from climate projections into land-use forecasts. Advance Bayesian and ensemble learning approaches for non-stationary temporal processes. Implement probabilistic diffusion or generative models
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has a strong background in control engineering, with documented expertise in optimal control, adaptive control and online optimization, stochastic systems, Bayesian inference, and state estimation and
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MOFs and their hybrid materials. The primary objective is to explore the fundamental structure–property relationships that govern MOF vitrification and liquid-phase behavior, and to develop new MOF-based
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Science or Mathematics. An ideal candidate has a research track record at a high international level (reflecting the academic age of the applicant) and experience with mechanising theory and/or practice
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& Atmospheric Physics Division of the Department of Space Research and Space Technology (DTU Space), current focus areas cover exoplanets, large-scale structure of the universe, physics of compact objects, space
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& Atmospheric Physics Division of the Department of Space Research and Space Technology (DTU Space), current focus areas cover exoplanets, large-scale structure of the universe, physics of compact objects, space
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of economics, i.e. microeconomics, macroeconomics, and quantitative methods. The department has a strong research profile and an excellent track record in fundraising. It is especially active in
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monitoring scientific progress and ensuring that project objectives and milestones are met. Your competencies The candidate must have a strong background in control theory, reinforcement learning, and game
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, computer science, statistics, epidemiology, psychology, public health, psychiatry or a connected field Excellent research track record, including at least two first-authored peer-reviewed publications relevant