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-performance computing (HPC), distributed and scalable computing, compiler technology, and algorithm optimization. Additional expertise in applying AI/ML to electromagnetic spectrum operations is desired. Duties
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. Distinguished Faculty Mentors: Carnegie Bosch Fellows will benefit from the unique opportunity to be mentored by pioneering leaders in their respective fields. Prof. Tom Mitchell (Founders University Professor in
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on developing innovative algorithms and models to address sophisticated problems in diverse fields such as robotics, healthcare, and finance. The department offers a range of undergraduate and graduate programs
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on developing innovative algorithms and models to address sophisticated problems in diverse fields such as robotics, healthcare, and finance. The department offers a range of undergraduate and graduate programs
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-performance computing (HPC), distributed and scalable computing, compiler technology, and algorithm optimization. Additional expertise in applying AI/ML to electromagnetic spectrum operations is desired. Duties
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at the intersection of machine learning and game theory. The RA will assist in developing and analyzing algorithmic models of strategic behavior, incentive design, and decision-making under uncertainty. Core
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at the intersection of machine learning and game theory. The RA will assist in developing and analyzing algorithmic models of strategic behavior, incentive design, and decision-making under uncertainty. Core
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. Distinguished Faculty Mentors: Carnegie Bosch Fellows will benefit from the unique opportunity to be mentored by pioneering leaders in their respective fields. Prof. Tom Mitchell (Founders University Professor in
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supervised by, Prof. Granger Morgan at CMU as well as by Prof. David Victor in the School of Global Policy and Strategy at UC San Diego (UCSD). Occasional travel to UCSD may be required. Applicants should
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checks that comply with IRB/ethical standards. Design and implement computer-vision algorithms. Develop, test, and refine deep-learning models (e.g., detection, segmentation, tracking) in PyTorch