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Field
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resources and received a $457M award from the National Science Foundation to create a new leadership-class computing facility. UT also leads the $20M NSF AI Institute for Foundations of Machine Learning
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of structured and unstructured data. - Support visualization of machine learning outputs including model performance, feature importance, confidence metrics, clustering, classification, and anomaly detection
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to ETH's central compute and the Alps supercomputer at CSCS A multidisciplinary team of mechanical engineers, materials scientists, biologists, and machine learning researchers, embedded in the broader
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: The successful candidate will have a PhD, or equivalent research or industrial experience at a comparable level, in Computer Science, AI, Robotics, Machine Learning, Software Engineering, Scientific Computing
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workload intensity, memory access patterns, and GPU utilization for real-time optimization. • Develop and deploy machine learning models to predict optimal voltage–frequency operating points during LLM
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in Machine Learning, Computer Science, Electrical Engineering, Geophysics, Applied Mathematics, or a closely related field. Demonstrated strong research skills, evidenced by high-quality publications
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-time RF machine learning, automotive cybersecurity, and radar threat warning models. Validation of SDD products is accomplished in VTNSI’s state-of-the-art 5,000 sq. ft. of wireless testing and lab
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or machine learning infrastructure, model deployment environments, or GPU-enabled systems. Familiarity with CI CD pipeline design and automation. Experience with hybrid infrastructure spanning on-premises and
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the speed up from using GPUs as well as machine learning techniques, e.g. simulation-based inference. Finally, we will use similar techniques to make a statistical inference of the population of subhaloes by
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, materials scientists, biologists, and machine learning researchers, embedded in the broader robotics ecosystem at ETH Zurich Strong ties to industry and to our spin-offs, and support for turning your research