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in practice. Our most recent new line of research is on how to verify the correctness of state-of-the-art algorithms for combinatorial optimization. Such algorithms are often highly complex, and even
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of state-of-the-art algorithms for combinatorial optimization. Such algorithms are often highly complex, and even mature commercial solvers are known to sometimes produce wrong results. Our work on designing
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of research is on how to verify the correctness of state-of-the-art algorithms for combinatorial optimization. Such algorithms are often highly complex, and even mature commercial solvers are known to sometimes
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Tenure-track Assistant and Associate Professorship positions in Data Science and Machine Learning...
Statistics section, include: Algorithms , focusing on online and approximation algorithms, graph and parameterized algorithms, string algorithms, data structures, combinatorial optimization, algorithmic
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University of North Carolina at Charlotte | Charlotte, North Carolina | United States | about 8 hours ago
transdisciplinary research interests in: 1. Modern and future Computing systems, including photonic and quantum computing, analog neuromorphic computing, combinatorial algorithms, and continuous optimization. 2
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outstanding research potential in all fields of Discrete Mathematics with emphasis on Structural Graph theory, Extremal Graph Theory, Combinatorial Optimization, Matroid Theory, and Algorithms. This appointment
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, discrete/combinatorial optimization, optimization modeling, etc., including their applied fields) Teaching and research guidance for subjects related to the above specializations within the Graduate School
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which 37 are Faculty members and 50 PhD candidates. Work tasks/research field We are seeking a candidate with a strong research profile in combinatorial optimization who can contribute to excellence in
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Statistics section, include: Algorithms , focusing on online and approximation algorithms, graph and parameterized algorithms, string algorithms, data structures, combinatorial optimization, algorithmic
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modeling, power/performance optimization, and energy-aware scheduling; Knowledge of optimization methods (e.g., convex optimization, combinatorial optimization, reinforcement learning) for energy management