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Concrete is one of the most widely used materials in construction, but its production significantly contributes to CO₂ emissions. To address this issue, our project focuses on creating supplementary
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. Thermoplastic composites, combined with novel material architectures, are central to this transition, enabling recyclable, lightweight structures. Research now targets energy-efficient, automated processes and
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the electronic structure and to realise artificial matter with exotic electron ground states. Another promising route towards physical implementations of energy-based machine learning and neuromorphic hardware is
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these structures are regulated. However, mechanisms that regulate other abundant alternative DNA conformations, including hairpins, cruciforms, and triplexes, remain poorly understood. In this project, we aim
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of this Research Fellowship is to contribute to a deeper, theory-driven understanding of the global Earth observation (EO) market and industry, identifying its structural dynamics, competitive landscape and long
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Bayes factor hypothesis tests in factorial designs. What are you going to do The envisioned projects will focus on the following activities related to Bayesian inference in factorial designs: Construction
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structure, your supervisor will be Prof.dr. M. Boon and co-supervisor Dr. Miles MacLeod. We are seeking a highly motivated Postdoctoral Researcher to join the DECIDE project at the University of Twente
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, are central to this transition, enabling recyclable, lightweight structures. Research now targets energy-efficient, automated processes and machinery to deliver high-quality components for the next generation
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high-fidelity CFD–machine learning (CFD–ML) framework capable of efficiently analyzing and optimizing rooftop aerodynamic duct structures for building-integrated wind energy systems. The aim is to push
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characterisation techniques; You will identify and implement optimal methods for the integration and testing of materials in real-life conditions; You will perform structure-property correlations and unravel