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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 3 hours ago
is designed for candidates who have completed their PhD within the last two years and have experience as postdocs or industry researchers. This position offers a 12-month term with potential
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for the heliums (3He and 4He) and their mixtures over a temperature range from 10 mK to 300 K and a pressure range of 0 to 30 bar (where these substances are fluid). The project will also have a parallel effort to
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scales through component-based parallelism. The focus of the post-doc position is to design and implement this pipeline -- developing native reasoning for rich combinatorial constraints, creating tools
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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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models. Experience in large-scale deep learning systems and/or large foundation model, and the ability to train models using GPU/TPU parallelization. Experience in multi-modality data analysis (e.g., image
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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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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 1 month ago
is designed for candidates who have completed their PhD within the last two years and have experience as postdocs or industry researchers. This position offers a 12-month term with potential
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. Promote equal opportunity by fostering a respectful workplace – in how we treat one another, work together, and measure success. Basic Qualifications: A PhD in materials science, mechanical engineering
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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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leading peer-reviewed journals and conferences. Researching and developing parallel/scalable uncertainty visualization algorithms using HPC resources. Collaboration with domain scientists for demonstration