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computing and scalable algorithms • Decision science and learning health systems design Qualifications Required: • Ph.D. in Systems Engineering, Industrial Engineering, Operations Research, Computer Science
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, computer vision and machine learning algorithms. · Information dissemination and decision-support services · Policy related analysis and investigation · Previous interactions with transportation funding
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 3 months ago
reusable libraries in Python, Scala, and/or C++, taking advantage of Spark, Hadoop, and other tool stacks as appropriate. * Developing computational and algorithmic approaches to understanding
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interactions, nuclear structure and reactions, electroweak structure, and lepton-nucleus scattering. The candidate will contribute to advancing statistical and computational algorithms to extend the capabilities
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, fined tuned for zooming in on machine spatial reasoning, is within the scope of this project. Developing efficient algorithms for converting computer simulations of a system in a complex environment (e.g
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closely with a small, dedicated team, you will: Design and implement SLAM-based navigation algorithms for GPS-denied forest environments (45%) Develop multi-sensor integration software for LiDAR, cameras
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structure-preserving discretization algorithms (a refinement of finite-element analysis compatible with exact geometric, topological, and physical constraints) with artificial neural networks for achieving
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the JAX Python library, including efficient implementations of classical numerical algorithms. 2. Extend the hybrid FEA-ML framework to include nonlinear cohesive zone models with simple traction separation
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or accelerated acquisition and reconstruction algorithms will be highly valued. Instructions Interested candidates should apply via Interfolio link with their CV (including a full list of publications), a
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on circular economy research Experience in working in the genetic algorithm and artificial neural networks is preferred. Experience in manufacturing process modeling of advanced manufacturing technologies