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learning representations and improve their interactivity. Make AI explanations more understandable Machine learning algorithms often appear as complex black boxes and much research goes into visualizing
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on developing machine learning algorithms to support the use of complex urban simulators in decision-making under uncertainty. This PhD project shifts the focus from optimality to relevance in urban land-use and
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process modelling, experimental data, model parameters and modelling approaches in order to optimize design, analysis and operation of complete capture processes. The goal of the project is to develop
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solvers and optimization algorithms for 1 year and 4 months. The Section of Solid Mechanics conducts research and teaching in the fields of structural and materials mechanics, vibration and their active
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create a closed loop pipeline able to rapidly design binders to any target and optimized for developability. The program is rooted in DALSA (DTU’s Arena for Life Science Automation), a new
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initiatives towards the development of new environmentally friendly products, cleaner and more sustainable manufacturing and farming processes, new medical treatments, and richer biodiversity and ecosystems. In
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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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position to develop and apply emulators for Earth System Models (ESMs), with a particular focus on the role of the biological carbon pump in oceanic carbon cycling and climate feedbacks. The research will
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will explore reaction evolution mechanisms for magnesium-based binders, leveraging state-of-the-art experimentation and numerical modelling tools. This position is part of the prestigious Villum Synergy