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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 months ago
to contribute to innovative projects at the forefront of healthcare delivery improvement, leveraging Large Language Models (LLMs) and clinical data analysis. About the Position Our Postdoctoral Research Program
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. Previous experience in computational modeling of atmospheric aerosols and parallel computing/software development is strongly desired. The term of appointment is based on rank. Positions at the postdoctoral
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differential equations. The individual must be able to immediately program in the software SFEMaNS using object-oriented Fortran 2033 and MPI. The individual must be familiar with the linear algebra parallel
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-disciplinary research team, consisting of researchers in coil development, electromagnetic simulation, parallel transmit RF pulse design, pulse sequence development, advanced MR image processing, analysis and
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the Computer Science program at New York University Abu Dhabi, seeks to recruit a research assistant to work on the intersection of compilers and deep learning. Many companies, such as Google, Facebook, and Amazon
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with implementation of: existing algorithms and computer software for analyzing omics-based data sets [high-throughput, massively parallel genomic/proteomic/clinical.]; data management and analysis
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programming would be welcome (CUDA). Required Documents Resume To be considered for this position please visit our web site and apply on line at the following link: https://hr.caltech.edu/work/job_openings We
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Brookhaven National Laboratory is committed to employee success and we believe that a comprehensive employee benefits program is an important and meaningful part of the compensation employees receive. Review
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job discipline. Certifications: --- Preferred Qualifications Experience: Experience leading the design, development and publication of innovative projects in AI/ML solutions. Relevant programming
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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