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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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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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research environment focusing on integrating multi-source data and developing novel algorithms to address the challenges posed by global environmental change. You will focus on integrating experiments, field
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-based simulation model for assessing future mobility technologies in the Greater Copenhagen region. Explore the development of machine-learning based scenario discovery for future mobility policy design
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interface, and all the way to quantum algorithms and applications. The long-term mission of the programme is to develop fault-tolerant quantum computing hardware and quantum algorithms that solve life
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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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(entities) given the rules and the rules given the molecules. The aim of this project is to develop a theory and accompanying algorithms to decide if an abstract system can be instantiated by a concrete
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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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better and faster decisions when assessing funding applications, ensuring the efficient and unbiased elimination of poor applications? This question can be addressed through training algorithms on past
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the loop and using active learning to determine which demonstrations to collect. The candidate would work on both projects and be responsible for: Implementing AI and probabilistic ML algorithms Development