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performance and funding availability. VISION Position Purdue as a national leader in AI-driven software and systems research spanning edge computing, cloud and distributed systems, high-performance
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Computing's mission is to harness advances of the computing age, by transforming the capabilities of academia in all key areas; computing fields – supporting the rapid growth and evolution of computer
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optimization, and application-driven performance analysis for HPC, scientific Artificial Intelligence (AI), and scientific edge computing. We are a leader in computational and computer science, with
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Ridge Leadership Computing Facility (OLCF). This position resides in the Advanced Computing for Chemistry and Materials Group in the National Center for Computational Sciences (NCCS), Computing and
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HVAC, water heating, and data center cooling systems Work across system-level and component-level design challenges Integrate predictive and optimization techniques to enhance system efficiency
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leadership- class computing for science and engineering; focuses on grand-challenge science and engineering applications; procures largest-scale computer systems (beyond typical vendor design points) and
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King Abdullah University of Science and Technology | Saudi Arabia, | Saudi Arabia | about 10 hours ago
algorithms and tools for problems of relevance to Vision-CAIR research group and Visual Computing Center (VCC) with a particular focus on learning efficiency, computational creativity, continual learning
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five-year mission of the ORNL Quantum Science Center (QSC) to establish a quantum-accelerated computing ecosystem for scientific applications. The successful candidate will develop, test, and evaluate
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design and optimization for building energy-efficient and robust brain-inspired, autonomous, and cognitive systems and intelligent vision systems, including efficient learning and inference of
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design and optimization for building energy-efficient and robust brain-inspired, autonomous, and cognitive systems and intelligent vision systems, including efficient learning and inference of