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. The work combines physics-based thermal design and process-level system simulation with high-fidelity computational fluid dynamics and fast reduced-order and machine-learning models, so that the final design
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of Prof. Georg Madsen, with regular shorter research stays at Aarhus University. The project combines density functional theory (DFT), machine-learned force fields and atomistic simulations to uncover how
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of detailed models for the design and optimization of pyrolysis plants in interaction with CO₂ capture.” You will drive the development of a comprehensive, modular simulation framework that links all major unit
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for the treatment of these diseases, with a focus on ion channels. Our work combines in vitro and in vivo model systems, human tissue analysis, hormone secretion assays, electrophysiology, (live) imaging, molecular
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to build and run advanced numerical models, carry out simulations and analyse results in a systematic and transparent way. Depending on ongoing projects, you may be involved in collaboration with industrial
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research profile is clearly connected to thermal Modelling, and you can demonstrate experience with CFD modelling. You have solid skills in modelling and simulation using Tools and data processing and
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dynamic models for simulation, analysis and control design, and you are motivated by developing and implementing model-based control solutions that must perform reliably in a real pilot-scale system. You
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. The tasks include battery cell characterization and modelling based on laboratory tests, and development of algorithms for estimating the charge level, health, and power capability which includes robustness
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nitrogen dynamics, and climate change mitigation potentials in agroecosystems. You will be contributing specifically to the area of regional simulation using process-based models and advanced statistical
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. The project focuses on the intersection of deep reinforcement learning, probabilistic modeling, and bio-inspired architectures (such as Spiking Neural Networks) to achieve sample- and energy-efficient robust