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Field
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it is important to assess their performance, sustainability and safety, to maximize their positive effects. The Unit achieves its objectives by combining pre-normative experimental research and desktop
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-thinking approach to your work, while enjoying the challenge of learning and mastering new tasks? Are you creative, curious, and motivated to optimize, develop, and implement new ideas? If so, you are
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or for multiobjective optimization problems. Implement the developed algorithms (e.g., in Python) and evaluate their practical performance on artificial and/or real-world data. Teach tutorials (in English) for
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5.2: Deep Eutectic Solvents (DES) are increasingly being studied for their potential role in lithium-ion battery recycling. DES has limitation for industrial application due to its poor performance in
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that merge thermo-fluid dynamic laws, deep learning, and experimental data. A central goal is to overcome current limitations in TES operation and optimization, enabling discovery of new high-performance and
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Reconfigurable/Spatial computing architectures, such as FPGAs, CGRAs, and AI accelerators, offer significant opportunities for improving performance and energy efficiency compared to traditional CPUs
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framework across a suite of practically relevant optimization problems in public transport planning and airport operations. The broader project also includes research on heuristic methods, in particular Large
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-assisted simulation framework by providing accurate high-fidelity numerical data for training and validation of surrogate models for multi-disciplinary design and optimization. · Participating in
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flexible alternative. Thanks to their significant thermal inertia and ultra-low-temperature operation, DHCNs can absorb surplus renewable energy, respond to market signals, alleviate grid stress, and provide
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focus on understanding how axons maintain their structure and function, and how these processes break down in disease. You will have the opportunity to contribute to one of our ongoing projects addressing