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network discovery and individualized circuit targeting, multimodal concurrent brain stimulation-recording, and computational modeling. We bridge invasive approaches, including intracranial electrical
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to this call may be national, foreign or stateless candidates holding a PhD degree in the field of Environmental Science and/or Engineering, Environment and Sustainability, Chemistry, Computer Science, Data
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to an aluminium drum resonator, referred to as a double-drum resonator [1]. The team has recently achieved in implementing reservoir-computing in the coupled drum resonators and successfully fabricated networks
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for this vacancy, this concerns a joint PhD position with KU Leuven as the main institution and primary workplace (and VUB as the partner institution).• Subject-matter expertise and an international research network
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Deadline 15 Oct 2026 - 00:00 (UTC) Country United Arab Emirates Type of Contract Other Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the
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PhD position in Experimental Physics with focus on photonics and materials science (applied aspects)
research in condensed matter physics, nanotechnology, photonics, ultrafast spectroscopy, and theoretical and computational physics. We announce a PhD position within the Marie Skłodowska-Curie Doctoral
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research in condensed matter physics, nanotechnology, photonics, ultrafast spectroscopy, and theoretical and computational physics. We announce a PhD position within the Marie Skłodowska-Curie Doctoral
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particular focus on technologies enabling quantum networks. The successful candidate is expected to build a vibrant independent research programme in quantum technology. The ideal candidate will also
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within the NIHR BioResource Centre Maudsley. The GLAD-MHG Omics Cohort, builds on the existing NIHR BioResource GLAD Study (>38,000 participants). The new programme will recruit an additional 12,000
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determines observables of the replication program such as the Mean Replication Timing (MRT) and the Replication Fork Directionality (RFD) profiles. We proposed a strategy to train a neural network to infer