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Requisition Id 16262 Overview: We are seeking a postdoctoral researcher to work at the intersection of tensor networks, quantum algorithms, scientific computing, topological physics, and quantum
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expected to bring expertise in all of them: Experience developing or implementing quantum algorithms for Hamiltonian simulation. Background in quantum materials or quantum many-body physics, particularly
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Requisition Id 16760 Overview: We are seeking an Applied Data Scientist to perform development of novel and advanced AI/ML algorithms. This work will support a broad user base by applying AI methods
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Postdoctoral Research Associate- AI/ML Accelerated Theory Modeling & Simulation for Microelectronics
Requisition Id 15685 Overview: The Center for Nanophase Materials Sciences (CNMS) is seeking a Postdoctoral Research Associate to support research directed towards developing novel AI/ML algorithms
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navigation, trajectory planning, and point cloud mapping; and (3) deploy the developed algorithms on a physical mobile robot operating in GPS-denied, low-light, and geometrically repetitive environments
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computational models, systems, and AI/ML tools using algorithms and analytics for materials and related physical sciences for a broad range of energy, transportation, and advanced manufacturing applications. In
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/C++, or similar languages. Ability to develop, validate, and document research prototypes, algorithms, and technical workflows. Strong written and verbal communication skills for interdisciplinary
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, patents, journal papers, and conference publications, participate in proposals, and estimate costs. Basic Qualifications: A PhD degree in physics, optical or electrical engineering, nuclear engineering, or
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electromagnetic transient (EMT) simulations, high-fidelity dynamic and transient power electronics resources modeling, explore different intelligence algorithms to enhance ease of usage of simulations, and
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) with a particular focus on event forecasting, predictive maintenance, and corrective actions. The role would encompass working on forecasting, predictive, and text generation algorithms with Large