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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | about 2 months ago
. The researcher(s) will be provided access to state-of-the-art supercomputing facilities with advanced GPU and data storage capabilities. Additionally, opportunities will be available for collaborations. Duties
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(PEBA), offers state employees a valuable benefits package, including health and life insurance, generous paid leave and retirement programs. To learn more about USC benefits, access the "Working at USC
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Ridge National Laboratory (ORNL). This position resides in the AI Operations Program office within the Application Development Division of the Information Technology Services Directorate. Our AI/ML
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and H100 GPUs, combined with pre-processed large-scale biobank data such as UK Biobank and ADSP, enabling you to work at the scale required for breakthrough research. The role offers exceptional
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communicate effectively with the team to debug the model. The OLG model codebase is technically challenging and computationally demanding. For example, it solves dynamic programming problems with GPU
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completed Ph.D. within the last 5 years in Computational Biology, Bioinformatics, Machine Learning, Artificial Intelligence, Virology, or a related field Strong programming skills in Python, R, or Julia, with
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DNN theory and the mapping of DNN models on a device. Experience in optimizing DNN models for deployment including pruning and quantisation. Experience with Generative AI Experience with programming in
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. Implement Docker-based deployment, memory footprint reduction, and GPU acceleration. Collaborate with algorithm and hardware teams for integration and benchmarking. Job Requirements: Master’s with experience
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; and scaling and optimising data science and AI research to leverage HPC and GPU infrastructure. Main Duties and Responsibilities 1. Provide leadership of software engineering services managed and
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mathematics and engineering. The Interpretable Machine Learning Lab has dedicated access to high-performance CPU and GPU computing resources provided by Duke University’s Research Computing unit and state