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research aimed at enhancing the efficiency and performance of PRO systems for sustainable energy solutions. PRO process is dealing with harvesting clean energy from the salinity gradient between different
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case is central to this position. SimMobility is based on activity-based mobility modelling theory, simulating agent-level behavior such as route, departure-time, and mode choice within an activity-based
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Foundation Center for Biosustainability at the Danish Technical University! This position is for you if you are an experienced scientist, and you are looking to make a difference being part of multiple
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the computational activities in a large closed-loop collaboration that includes computational, biotechnological and automation activities, requiring a solid understanding of the different areas involved in
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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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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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, generative design, building performance optimization, digital design methods (e.g., predictive modeling, multi-agent systems and algorithmic techniques for architectural design), digital design epistemologies
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electricity price signals, demand-response mechanisms, and time-of-use optimization. AI-Driven Optimization using Reinforcement Learning: Apply RL algorithms to develop and train agents that optimize power
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-capacitors, additive manufacturing, and energy technologies, in addition to the aforementioned. The department is an exciting environment, where you will be exposed to many different fields and have your
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algorithm. Design methods: Develop novel control methods for power electronic converters feeding electric machine Simulation: Learn advanced simulation tools such as Ansys to simulate and analyze the effect