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and macroscale structural dynamics. As a PhD candidate, you will focus on the identification and modeling of friction damping and energy dissipation in assembled structures. Your work will combine
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computational facilities. The powerful computational facilities enable us to develop advanced numerical models. The Department of Engineering Structures focuses on the development of resilient, smart and
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for pavement lifetime prediction, primarily in Dutch conditions. Foreseen challenges include data harmonization, key relationship extraction, scalability, metadata structuring, predictive modeling, and data
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on the development of new antenna and integrated circuit concepts for radar and electromagnetic modelling. The Electromagnetics (EM) and Integrated Circuit (IC) groups are involved as well as the Center for Wireless
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of frictional behavior and advanced constitutive models to describe it. In this project, you will: Experimentally characterize ply-ply and tool-ply friction under a variety of processing conditions using our
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on the development of new platforms for osteoarthritis research, including advanced in vitro and ex vivo culture models. With these models we strive to reduce and replace animal experiments while working towards
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to relate model structure to outcomes and interpret them in biologically meaningful ways. A collaborative, curious, and creative mindset. Fluency in English (written and spoken; minimally language level C1
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the next generation of miniaturized in vitro models to advance regenerative medicine. Your colleagues: A highly interdisciplinary team at MERLN, working within the national MDR consortium. The project Since
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and trends. Using advanced economic models, we develop and analyze scenarios that support informed decision-making. Our work also focuses on designing new systems, governance structures, and incentives
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and research possible. PhD Opportunity: Engineering a Next Generation of Miniaturized In Vitro Models Our goal: Develop the next generation of miniaturized in vitro models to advance regenerative