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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
and Experience: Distributed parallel training and parameter-efficient tuning. Familiarity with multi-modal foundation models, HITL techniques, and prompt engineering. Experience with LLM fine-tuning
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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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completion) in applied mathematics, computer science, or a closely related field. Strong background in numerical linear algebra, algorithm design, and parallel computing. Proficiency in programming languages
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and optimize large-scale training and inference runs for foundation models on JUPITER (multi-GPU/node, mixed precision, parallelization, I/O optimization) Integrate multimodal data sources (e.g., scRNA
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and abilities Experience in massive parallel computations on HPC systems. LanguagesENGLISHLevelExcellent Research FieldAstronomy » AstrophysicsPhysics » OtherAllOtherYears of Research Experience4 - 10
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opportunities for parallelism of the completion process, highlighting the potential for significant speedup in computations. Job responsibilities Research and Development: Conduct research to develop novel
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paralleling SiC MOSFETs and power modules and must have experience with hardware methods to attenuate the oscillations successfully. Your experimental experience must be relevant to the tasks and obtained from
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of the 3D Biology research programme at the Radboud university medical center. You will also collaborate closely with scientists at HUB Organoids who are running parallel programmes and with researchers
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of interpretability methods to ensure ML outputs are meaningful in scientific contexts. Preferred: Background in biomedical data, healthcare, or AI for life sciences. Experience with parallel computing. Familiarity