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feed into this vision. The intended start date is July–August 2026. Job requirements PhD in machine learning, artificial intelligence, computational chemistry, computational materials science, or a
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approaches to remove atmospheric particulate (e.g., PM2.5) pollution. The math-based subgroup focuses on the use of deep learning and generative AI to address critical problems for the electric grid and broad
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data analysis methods to study biological memory circuits and their applications to machine learning. Building on recent work from the Fiete Lab, the role focuses on identifying principles of biological
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 10 days ago
Deep Learning), successful experimental collaboration experiences and excellent communication skills are preferred. Create a Job Match for Similar Jobs About The University of North Carolina at Chapel
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perturbation modelling. The ideal applicant brings not only strong technical skills, but also interdisciplinary knowledge on the subject. More precisely: PhD degree in computer science, machine learning
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transmutation of long-lived radioactive elements is a research area that complements deep geological disposal. Thus, the recycling and transmutation of minor actinides (MA) would reduce their radiotoxicity and
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transmutation of long-lived radioactive elements is a research area that complements deep geological disposal. Thus, the recycling and transmutation of minor actinides (MA) would reduce their radiotoxicity and
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. Publish and present findings in international journals and conferences. Supervise and mentor junior researchers and students. Profile PhD in Computer Science, Biomedical Engineering, Data Science, or a
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this position, you will ideally bring the following: For the Networking and AI position: Completed a PhD in Computer Science, Electrical & Computer Engineering, or a closely related field Expert knowledge of deep
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paper, including the previous developments, should be published and our group established as one of the players in the area of deep-learning for computational mechanics. Legislation and Regulations