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Field
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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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to algorithms, and cross-platform co-design across superconducting, neutral atom, and diamond-based systems, guided by quantitative resource estimates targeting DOE-priority scientific applications. Position
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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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members. Participate in traffic scenario generation project and pedestrian modeling project. Develop sophisticated AI-driven algorithms that create realistic, safety-critical test scenarios for autonomous
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by working to develop novel algorithms on finite element method, isogeometric analysis, geometric modeling, machine learning and digital twins to study various applications such as computational
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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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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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: Research (65%) · Investigate literatures to identify existing solutions and the problem · Develop and apply state-of-the-art natural language processing algorithms and models to multimodal
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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