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Computational complexity in adiabatic quantum computation AI to boost quantum technologies (e.g., machine learning for quantum error prevention/mitigation/correction) Quantum machine learning Quantum cloud
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 1 month ago
universities in the nation for federal research expenditures as well as for federally funded social and behavioral sciences research and development. Here at Carolina, our highly skilled postdocs play a vital
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the next generation of secure agentic AI systems through cutting-edge research in adversarial machine learning and formal verification. The Role As a research scientist, you will contribute to frontier AI
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. The postdoc position is for until Dec 31, 2026. The research is in the broad area of graph data management, mining, systems, and machine learning on graphs, including knowledge graphs, social and biological
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the area of enzyme engineering to the next level, while having a positive impact on our world. When joining our team, you get the opportunity to use the latest algorithms in machine learning for improving
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the Division of Data Science and Artificial Intelligence and the employment is with Chalmers University of Technology. The division’s research spans from foundational machine learning theory to applications
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(lola.adedokun@uky.edu). More information on benefits can be found here: https://postdoc.uky.edu/current-postdocs/ Skills / Knowledge / Abilities The candidate should have excellent written and oral
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for the global challenges of today and tomorrow. Where to apply Website https://academicpositions.com/ad/eth-zurich/2025/postdoc-in-tree-physiology-and… Requirements Research FieldBiological sciencesYears
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applications from strong and creative researchers in machine learning or optimization who are eager to engage in a collaborative and interdisciplinary environment. Postdocs in our group are encouraged to pursue
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work to discover cell specific isoforms and relate them to pathological signatures and genetic risk factors in ALS patient tissue. You will apply supervised and unsupervised machine learning methods