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will work closely with Drs. Audrey Hendricks and Ryan Layer as well as grad students, postdocs, and other collaborators to develop and implement statistical and machine learning methods, software
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level of work performed by people assigned to this classification. They are not intended to be construed as an exhaustive list of all job duties performed by the personnel so classified. Management
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, applications of machine learning to particle phenomenology, and lattice QCD, both within the Standard Model and beyond. The particle physics phenomenology group members are: J. F. Kamenik (head), B. Bajc, S
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-edge sensors with current computational tools and machine/deep learning. As a Postdoctoral Researcher, you will be a key member of our team responsible for designing the seamless integration
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Enhanced Learning Dept., (iii) the Dept. of Applied Social Studies, and (iv) ‘A Healthy MTU’ (i.e., MTU’s Healthy Campus Programme). The postdoctoral researcher will coordinate the planning, implementation
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networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical and algorithmic perspectives on large language models Statistical learning theory and complexity analysis
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, United States of America [map ] Subject Area: Computational Science / Artificial Intelligence/Machine Learning Appl Deadline: (posted 2025/11/19, listed until 2026/01/26) Position Description: Apply Position Description
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candidates with strong expertise in Bayesian methods, uncertainty quantification, and/or machine learning applied to nuclear theory. The group’s research spans a wide range of topics including nuclear
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) and genetics data which are measured by longitudinally and cross-sectionally. • Developing and applying machine learning and AI approaches to identify interactive topological relationships
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analysis to translate THz signals into optical material properties such as refractive index and absorption coefficient. Development of machine learning algorithms for material classification. Exploration