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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 20 days 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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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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Station. In parallel, we pursue R&D for future gamma-ray missions and play an active role in ESA proposals, including new-ASTROGAM, the envisioned successor to the Fermi-LAT satellite, and other compact
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the collective behavior of complex systems, understanding how micro- level interactions drive macro-level evolution. Practical experience with high-performance computing (HPC) and parallel processing to enable
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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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researchers and with members 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
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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
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for the electrochemical community. In this work, we aim to study the mechanical properties of electrodes during charge/discharge cycles, in parallel with the ionic insertion/deinsertion phenomena observed by
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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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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