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on the broader areas of cybersecurity, hardware security, privacy-preserving machine learning, and applied cryptography. Investigate methodologies, protocols and architectures for private and trustworthy
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to reconstruct subsurface defects; Implement image/signal‑processing or machine‑learning pipelines for automated flaw characterisation; Collaborate with the Federal University of Rio de Janeiro, including short
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is led by Professor Mauro Giacca and comprises over 65 clinical and non-clinical academic groups, hosting 400 personnel and 110 PhD students. Our community of world-renowned researchers and educators
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is led by Professor Mauro Giacca and comprises over 65 clinical and non-clinical academic groups, hosting 400 personnel and 110 PhD students. Our community of world-renowned researchers and educators
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), with two cross-cutting Themes (Basic and Clinical). The School is led by Professor Mauro Giacca and comprises over 65 clinical and non-clinical academic groups, hosting 400 personnel and 110 PhD students
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models and machine learning techniques for kinetic equations arising from plasma and neutron transport. The position will be based at Virginia Tech’s campus in Blacksburg, VA. The postdoc will have a
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must possess substantial experience in artificial intelligence and machine learning methods, specifically in AI-driven materials discovery, machine learning applications for materials, or generative AI
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. Preferred Qualifications • Experience with deep learning architectures applied to geophysical or environmental data. • Familiarity with physics-informed machine learning or hybrid modeling approaches
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is looking for a highly motivated and independent individual to work as a Postdoctoral Associate. This position is for a post-PhD trainee preparing for a research scientist career path. The planned
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simulations), computational chemistry, machine learning, enhanced sampling methods, force field development, or related fields are encouraged to apply. Start date is flexible. the postdoc will collaborate with