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As a flagship research center in nanoscience and nanotechnology, our mission is to open and explore new frontiers of knowledge at the nanoscale, and bring value to society in the form of new
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with strategic leadership of a multidisciplinary team of scientists and engineers. The GL advances the frontiers of nanoscience by conceiving, conducting, and disseminating high-impact experimental
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4 Dec 2025 Job Information Organisation/Company CNRS Department Institut matériaux microélectronique nanosciences de Provence Research Field Chemistry Physics Technology Researcher Profile
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). This is focused on development of GaN-, AlGaN- and AlN semiconductor devices, for power and RF applications. You will be working with setting up and managing chip fabrication and device modeling using
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The University of North Carolina at Greensboro | Greensboro, North Carolina | United States | 17 days ago
School of Nanoscience and Nanoengineering, facilities to support in vitro and in vivo models, a competitive startup package, and a spacious research laboratory. Minimum Qualifications: Ph.D. in Biology or
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setting up and managing chip fabrication and device modeling using TCAD and electrical measurement of components. There are also possibilities in directly interacting with other research projects within
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recognized programs in Energy and Sustainability, Catalysis and Reaction Engineering, Materials, Multi-Scale Modeling, and Biomedical Engineering. One of our faculty members directs the annual International
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The Theory and Modeling Group at the Center for Nanoscale Materials (CNM) seeks an outstanding Assistant Scientist to lead and support frontier research at the intersection of AI/ML, data
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synthetic polymers), including crosslinking methods (i.e., physical, chemical, ionic, photo-crosslinking); Experience in tissue regeneration models and applications; Experience with drug delivery systems
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finishing their MSc until January 2026 will still be consider eligible for evaluation. Experience and Technical Skills Characterisation of magnetic tunnel junctions; Modelling artificial neural networks