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role: This post, funded by an ERC Consolidator Grant (DISCO), focuses on the dynamic behaviour of ferroelectric domain walls using advanced cryogenic and electrically biased 4D scanning transmission
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; EPSRC Centre for Doctoral Training in Green Industrial Futures | Bath, England | United Kingdom | 3 months ago
? This PhD, working with Prof. Marcelle McManus , provides an opportunity to work to explore and advance novel decarbonisation solutions for high-energy use industrial sites. Your project will be co-created
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sensing, and Electromyography (EMG) tools to understand user-device interaction and optimize real-world rehabilitation performance. The student will gain experience in AI, human biomechanics, smart textiles
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inculcate interdisciplinary working in our next generation of researchers. Interviews will be online in late August/ early September. Project Description: The increasing use of encryption in IoT
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(i.e. red agents). However, due to a fragmented market, rapid technical developments, and nascent research the extent of capabilities and optimal solution architectures are not well understood. Current
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modelling tools (CST or HFSS) - Fabricate and test for optimal electromagnetic performance, such as bandwidth, return loss, insertion loss and power-handling. - Develop and characterize new bonding/alignment
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become the bottleneck in achieving optimal performance and trustworthiness. This project will focus on how a federated multi-task learning framework can be effectively designed and optimised to address
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Associate The Responsive Apparel Design (RAD) Lab at Cornell University is seeking a highly motivated and talented postdoctoral researcher to join a collaborative As part of Cornell University, a world-class
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seek optimal trade-offs between compactness and performance, delivering foundational insights into the future of high-performance electric propulsion systems. Funding 3-year PhD tuition fee (for UK home
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Decision Intelligence for Supply Chain and Operations Optimization. The successful candidate will contribute to cutting-edge research at the intersection of Statistical Machine Learning and Generative