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: Applications accepted all year round Details Fusion energy, a promising clean energy source, faces significant challenges, one of which is the containment of extremely high-temperature plasma. Magnetic fields
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workflows for engineering design and optimization of fusion reactors and facilities needed on the path to a Fusion Pilot Plant (FPP). The Principal Research Scientist (Managing) will also deploy and use those
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Oracle Fusion Cloud Developer to support the University's cloud-based Human Capital Management (HCM). The Oracle Fusion Cloud Developer will be responsible for providing technical solutions
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, Chinese Academy of Sciences, jointly invite applications for 10 postdoctoral positions in theoretical and/or computational study of magnetic confinement fusion plasmas. The starting date is negotiable. The
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Physics and Fusion Science, and a program for MS and PhD in Medical Physics is in the planning stages. The metropolitan hub of Hampton Roads provides regional opportunities to partner with National Labs
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Institut de Psychiatrie et Neurosciences de Paris (IPNP) | Paris 15, le de France | France | about 2 months ago
observations of cell-cell fusion events. The project will be done in close collaboration with Grégory Lavieu’s lab (https://u-paris.fr/lavieu-lab/ ). References [1] Jackson, C. B., Farzan, M., Chen, B. & Choe, H
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Multi-Sensor Data Fusion and Smart Decision Making School of Electrical and Electronic Engineering PhD Research Project Self Funded Prof Lyudmila Mihaylova Application Deadline: Applications
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airport environments (ASTONISH project) and to space situational awareness applications (ISTAR and EMISSARY projects). Where to apply Website https://www.unina.it/it/ateneo/concorsi-e-borse-di-studio
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fusion applications. Postdoctoral Researcher or Engineer (all genders) Heavy-Ion Beam Irradiation Platform for Fusion Materials Posting ID: 26.37-1600 We are looking for applicants with a: PhD or
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 8 hours ago
data to learn transferable structural embeddings, creating a foundational model for forest structure representation learning. A second thrust centers on multi-modal fusion, designing and training shallow