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Field
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architecture. Programming skills in Python, ROS, TensorFlow, or MATLAB. Strong interest in applied AI and autonomous systems development. Application materials: CV, cover letter, and one recommendation letter.
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architectures Collaborate across interdisciplinary teams spanning biology, chemistry, computer science, and applied mathematics Publish findings in high-impact journals and present at leading conferences Required
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Confidential Computing and Secure Multi-tenancy. The candidate will be able to make research contributions in areas of system software architectures to support secure computing enclaves on large scale HPC and
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for AI and deep learning (details: NVIDIA DGX-2) Intel-based Aurora Supercomputer: A next-generation supercomputing system (details: Aurora Supercomputer) Additional advanced compute architectures designed
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accurate prediction of caries lesion progression to support precision prevention and treatment planning. The postdoc will lead efforts to design multimodal learning architectures (e.g., attention-based
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of medicine and engineering, with an emphasis on precisely controlling biomaterial functionality and architecture to treat diseases, control cell behavior, or answer fundamental biological questions. Our
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with next-generation computing architectures. Main Tasks and responsibilities: Perform research related to the fields of: advanced memory concepts; voltage control of magnetism; interplay between surface
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experience with deep learning frameworks like TensorFlow, PyTorch, or JAX. In-depth understanding of transformer architectures, attention mechanisms, and fine tuning techniques for LLMs. Experience with time
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on HPC computer architectures and deliver them as user-friendly software to meet DOE experimental facility needs. We’re here for the same mission, to bring science solutions to the world. Join our team and
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. · Develop and characterize novel electrode materials, electrolytes, or battery architectures. · Utilize advanced electrochemical techniques (e.g., CV, EIS, GITT, PITT) and materials characterization tools