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- Delft University of Technology (TU Delft)
- Delft University of Technology (TU Delft); Published yesterday
- Delft University of Technology (TU Delft); yesterday published
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- University of Twente (UT)
- Delft University of Technology (TU Delft); Published today
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- Eindhoven University of Technology (TU/e); today published
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- University of Twente (UT); Published yesterday
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applications. These smart winglets are designed to optimize aerodynamic performance by responding to temperature variations and incorporating active thermal control for real-time configuration adjustments
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6 Dec 2025 Job Information Organisation/Company University of Twente (UT) Research Field Engineering » Control engineering Engineering » Electrical engineering Researcher Profile Recognised
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learning, to accelerate CFD optimization and enable adaptive control strategies for complex urban wind conditions. From an industrial standpoint, the objective is to deliver a cost-effective and efficient
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of these two postdoc positions is to contribute to the modelling, simulation, and optimization across different LDES technologies, from device (ESS+inverter) to system level (grid integration) under different
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Modelling, Hidden Markov Models, Causal Discovery Algorithms, Reinforcement Learning) Proficiency in R, Python, and/or Mplus. Excellent command of English (spoken and written) Your scientific qualities
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conditions. To achieve this, the project explores advanced machine learning approaches, including surrogate modeling and reinforcement learning, to accelerate CFD optimization and enable adaptive control
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to an ongoing project on the design and manufacturing of adaptive winglets using Shape Memory Alloys (SMAs) for aerospace applications. These smart winglets are designed to optimize aerodynamic performance by
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on the chemical, thermal and mechanical behaviour of burden materials. Delivering scientific insights and practical control strategies to minimise the circulation of volatile elements and their detrimental effects
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(DSO) to resolve local congestion issues. The design of such an edge decision-making platform requires an understanding of distribution systems operation as well as machine learning and optimization
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research on topics including, but not limited to: Continual learning, with a focus on novel optimization techniques and memory architectures. Advancing the reasoning capabilities of large language models