20 big-data-and-machine-learning-phd positions at Technical University of Denmark in Denmark
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recent large-scale capabilities in physics. Reliability, exploring uncertainty quantification and robust inference in machine learning. Explainability, leveraging identifiability and unique recovery
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and exact optimization methods enhanced by machine learning (ML). The overarching goal is to solve large-scale combinatorial optimization problems more efficiently, particularly in domains such as
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, the CAPeX approach to finding new electrocatalytic materials for energy conversion reactions uses state-of-the-art machine learning techniques, but experimental feedback is needed to improve the models and
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(e.g., based on physiological signals or direct inputs from occupants) and developing algorithms, including machine learning methods. The work will include statistical modelling, data-driven modelling
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externalities of transport. The division is interdisciplinary with scholars originating from transportation engineering, economics, psychology, computer science, social data science, machine learning, mathematics
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, including artificial intelligence (AI), machine learning, internet of things (IoT), chip design, cybersecurity, human-computer interaction, social networks, fairness, and data ethics. Our research is rooted
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simulations using, e.g., COMSOL, Lumerical, or other Maxwell solvers. Experience with machine learning algorithms is an advantage but not required. General qualifications Scientific production and research