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, but current methods are not always efficient or optimal. The process lacks an intelligent, informed approach to selecting the best grinding parameters, which can lead to inefficient maintenance actions
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on performance and safety, for example, through the efficient computation of Lyapunov and barrier functions, forward and backward reachable sets, optimal value functions etc. The broad goal is to build upon recent
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Develop practical, industry-transforming technology in this hands-on PhD program focused on immediate industrial applications. This exclusive opportunity places you directly at the interface between
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Associate Support development and documentation of Python-based Design Tool workflow for D-Suite Project Formulate and solve optimization problems, developing algorithms which have appropriate accuracy
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, interpretable models from experimental and operational data. The core goal is to balance model accuracy with computational efficiency, while meeting the needs of experimental validation. The framework will
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on Artificial Intelligence (AI), Deep Reinforcement Learning (DRL), and Predictive Maintenance for optimizing wind turbine performance and reliability. This research will develop an AI-powered wind turbine
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Research Assistant/Associate in Photonics Integration of Graphene and Related Materials (Fixed Term)
investigate the large area production of graphene, BN, MoS2 and other layered materials, optimize their transfer process in view of their application in energy, electronics and photonics. This will include
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on performance and safety, for example, through the efficient computation of Lyapunov and barrier functions, forward and backward reachable sets, optimal value functions etc. The broad goal is to build upon recent
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. The project focuses on power-aware computing, thermal optimization, and sustainable electronic design, targeting critical applications in aerospace, healthcare, and industrial automation. Hosted by the renowned
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Engineering, and Engineering Management. Students with interests in computational mechanics, optimization design, bioinspired design, sustainability management, machine learning, AI, uncertainty quantification