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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 hours ago
for extension based on mutual interest. We are looking for individuals with a strong theoretical and practical background in large language models, machine learning, and natural language processing, combined with
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. A 129, pp. 6470-6481; J. Chem. Theor. Comp. 21, pp. 6305–6314 (2025). Duties and Responsibilities Carry out research in theory and code development and the processing of large-scale simulations using
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within a multi-disciplinary research environment consisting of computational scientists, applied mathematicians, and computer scientists to link models and algorithms with high-performance computing
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, Integrity, Teamwork, Safety, and Service. Promote equal opportunity by fostering a respectful workplace – in how we treat one another, work together, and measure success. Basic Qualifications: A PhD in
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. Work in interdisciplinary collaborations with subject matter experts on various aspects of scientific data generation and processing and methods evaluation. Formulate high-quality research ideas and
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, parallel storage systems and scientific data management. Recent research project details and outcomes can be found in computer systems conference proceedings, such as HPCA, FAST, SC, DSN, HPDC, IPDPS, and
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, finite volume, and machine learning to solve challenging real-world problems related to structural materials and advanced manufacturing processes. The successful candidate will have experience with
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team of undergraduate/postgraduate researchers. Candidates should be able to multitask parallel evolution experiments with phenotypic and genomic analyses. Job Duties and Responsibilities: Typical tasks
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 30 days ago
for extension based on mutual interest. We are looking for individuals with a strong theoretical and practical background in large language models, machine learning, and natural language processing, combined with
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leading peer-reviewed journals and conferences. Researching and developing parallel/scalable uncertainty visualization algorithms using HPC resources. Collaboration with domain scientists for demonstration