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passive and active compensation methods exist, thermal effects must be addressed during early-stage design. This calls for advanced modeling techniques that capture thermo-elastic interactions with high
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trading decisions under high price volatility. This PhD position focuses on designing, developing, and evaluating self-learning energy trading algorithms that are able to cope with these challenges. By
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August 2025 Apply now This PhD position is part of the NWO-funded research project TACIT (Inclusive Technologies for Access and Social Participation). The project focuses on the co-design of inclusive and
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on the theoretical and algorithmic development of control methods that combine physical modeling and real-time computation. The work will involve deriving reduced-order models, designing controllers that exploit
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. The focus will be on designing reproducible techniques for identifying dependency structures, validating these methods through large-scale experimentation, and evaluating how well DNS configurations align
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models, designing controllers that exploit the system’s distributed dynamics, and validating them experimentally on soft robotic platforms in our lab. These include tentacle-like manipulators and soft arms
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addresses the following central research question: how can we design human-AI collaboration to mitigate biases and foster equitable decision-making? To answer this question, the research will explore areas
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setting builds on knowledge and theories at the intersection of innovation studies, construction management, and public management with many opportunities to collaborate with other research fields related
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unclear strategies for bias mitigation limit its effectiveness in practice. This PhD project addresses the following central research question: how can we design human-AI collaboration to mitigate biases
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decisions. The challenge then is how to optimally leverage such an asset to make viable trading decisions under high price volatility. This PhD position focuses on designing, developing, and evaluating self