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Field
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with health/real grid data or machine learning is beneficial but not mandatory. We Offer One year funded of 3-year PhD position (SIF Doctoral Student Grant). Access to high-performance computing
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Engineering, Computer Engineering, Computer Science, AI, Data Science, or a closely related discipline. Strong research background in machine learning, optimization, AI systems, or data analytics. Experience
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CRC2 & Mitchell Funded Tenure-Track Faculty Positions in Supercomtuping and High-Performance Computi
Queen's University - Electrical and Computer Engineering, Smith Engineering | Kingston Downtown, Ontario | Canada | about 20 hours agoprovided by the Engineering Teaching and Learning Team, the Queen’s Centre for Teaching and Learning, the Department, and Smith Engineering. The Department of Electrical and Computer Engineering has 36 full
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Data (CRCD), which operates a large multi-thousand-core cluster with GPU co-processors and PhD-level consulting staff. Pitt ECE also leads the NSF Center for Space, High-performance, and Resilient
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Data (CRCD), which operates a large multi-thousand-core cluster with GPU co-processors and PhD-level consulting staff. Pitt ECE also leads the NSF Center for Space, High-performance, and Resilient
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Qualifications PhD in Electrical Engineering, Computer Engineering, Computational Science, Computational Engineering, Aerospace Engineering, Computer Science, Math, Physics, or related discipline. Experience
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developing new machine learning methodologies that tackle unique computational problems in healthcare applications. We use large real-world complex datasets, including data extracted from electronic health
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Engineering Department at The Pennsylvania State University. This position involves the development and application of numerical analysis approaches using Machine Learning based multi-physics tools
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learning, transfer learning, foundation models, and self-supervised learning. Experience in dealing with large medical datasets (e.g., electronic health records data or medical images) Ability to use high
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experience involving data pre-processing and preparation for machine learning models Demonstrable research experience in conducting experiments for training and evaluating deep neural networks Knowledge