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integrated circuits for next-generation UOWC technologies. As a participant of the project, you will become part of a team at DTU with expertise in design, simulation, nanofabrication, characterization, and
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corrosive stimuli/loads thus laying a knowledge-based foundation for design of AM-defect tolerant microstructures. The work will be based on experimental characterization guided by advanced AM simulations
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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 co-simulation
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capability to model complicated magnetic systems for two reasons. First, all magnetic sources in a simulation interact, leading to computational resources scaling with the number of sources squared – which is
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tolerant microstructures. The work will be based on experimental characterization guided by advanced AM simulations. The latter being the responsibility of other participants of the MicroAM project
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co-simulation platforms (e.g., TRNSYS-Python) Implement and test AI-enabled smart energy management strategies in real-world settings Conducting in situ measurements, including planning, setting up
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-resolved micro and nano X-ray CT imaging of cement mixing processes. This is challenging due to the fast-evolving chemistry in the early mixing phase and low X-ray contrast between mixing and intermediary
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), electroencephalography (EEG), and behavioral paradigms, and apply signal processing and machine learning techniques to analyze neural and physiological data. In addition to the research tasks, the postdoc will contribute
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) simulations, which will be instrumental to the overarching goal of the project. The main focus of the simulations will be on two-phase flows with complex topologies in electrolyzer manifolds. OpenFoam is the