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University of North Carolina at Chapel Hill | North Wilkesboro, North Carolina | United States | 19 days ago
Python programming skills, including experience developing services, APIs, automation, data-processing workflows, or research software. * Strong Unix/Linux programming and systems skills, including shell
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networks, as well as courses in Python, GPU programming, mathematical modeling and statistics, or equivalent. We are looking for candidates with: A solid academic background with thorough computational and
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clinical datasets, high-end GPU and storage infrastructure, international research collaborations, and dedicated funding for international conference participation. Supervision The PhD candidate will be
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, or a closely related discipline. Demonstrated experience in scientific programming, scientific software development, or data-intensive computing. Experience with Python and at least one high
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learning programming libraries such as TensorFlow, PyTorch, or JAX is a must and experience with GPU-based experimentation and cluster computing (e.g., Docker, Slurm) a plus. In addition to the above, there
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jet physics, heavy-ion collisions, and detector performance studies. Experience in scientific machine learning, deep learning, foundation models, or multimodal AI. Experience with GPU programming
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Centers: Surgery, General Surgery Postdoc Appointment Term: 1 year Appointment Start Date: November 1, 2026 Group or Departmental Website: https://med.stanford.edu/s-spire.html (link is external
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resources, applications, tools, and services for the broader research community to use data at scale to pursue scientific inquiry and accelerate discovery. Learn more at https://gdc.cancer.gov/, https
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MSCA Doctoral Network ( https://www.elevate-dn.eu/ ) and co-supervised by our partners at the university of Liège. Your tasks in detail: Develop an event-driven learning algorithm for latent reasoning
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HPC resources (NAISS) and local GPU infrastructure. This Postdoc position is part of the eSSENCE graduate school in data-intensive science. The school addresses the challenge of data-intensive science