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Professor of Power Electronics and Integrated Magnetics for Future High Performance Computing and AI
, particularly as applied to the fields of Information & Communications, Health & Life Sciences, Agritech & Food Security, Energy and Climate Mitigation, emerging fields such as quantum, and novel computing
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international partners and connections to industry. We are now looking for a postdoctoral fellow interested in microwave quantum technology based on hybrid semiconductor-superconductor devices. Subject
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faculty members with active research interests including observational, computational, and theoretical astronomy, experimental nuclear physics, quantum computing, and quantum gravity. Students are involved
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Programme? Horizon Europe - MSCA Is the Job related to staff position within a Research Infrastructure? No Offer Description SIMaP (Science et Ingénierie des Matériaux et Procédés – UMR 5266), a joint
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The School of Physics seeks to recruit a Partnerships Manager for its EPSRC funded Quantum Information Science and Technology Centre for Doctoral Training programme (https://www.qist-cdt.info
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faculty members with active research interests including observational, computational, and theoretical astronomy, experimental nuclear physics, quantum computing, and quantum gravity. Students are involved
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is looking for a PhD Student (f/m/d) - Electrochemical swing adsorption of carbon dioxide. Your tasks Performing quantum mechanical and ML-based simulations to determine CO2 adsorption energies
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electrical and optoelectronic measurements, including low-temperature characterization. We are rooted in condensed matter physics and quantum science. In the context of the the available position, we aim
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• Supervision of interns, collaboration with PhD students The team investigates logical foundations and formal methods for quantum programming languages leveraging on a strong expertise in linear logic, lambda
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superlattices (twistronics). The role will focus on developing and applying theoretical models and computational quantum chemistry and machine learning methods to uncover novel properties and phenomena in low