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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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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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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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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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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
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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
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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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Job Description The Institute of Mechanical and Electrical Engineering invites applications for a 24-month scholarship as a postdoctoral researcher in development of topology optimisation methods