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Requisition Id 16802 Overview: We are seeking a Postdoctoral Research Associate for the development and application of advanced multiphysics simulations, and machine learning (ML) methods relevant
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develop optical interconnects between different types of qubit systems. The goal of the project is to demonstrate a quantum network that enables remote entanglement and quantum teleportation between atomic
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supercomputer, the world's first exascale computing system. This is a unique opportunity to engage in transformational research that advances the development of AI-ready scientific data, optimized workflows, and
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of accomplishments (e.g., peer-reviewed publications and/or patents). Key Responsibilities & Accountabilities: Algorithm Development, Data Analysis and Communication: Effectively design, implement, and evaluate
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(CAMERA) has a new opening for a postdoctoral scholar to develop cutting-edge mathematics and algorithms to analyze complex data from Department of Energy (DOE) experimental facilities. This role involves
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systems in firmware algorithm development and simulation. We are also active in R&D using AI in custom electronics as well as in distributed computing. The Pitt ATLAS Group is led by Professors Joe Boudreau
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A postdoctoral position is immediately available at the Advanced Photon Source (APS) at Argonne National Laboratory. The successful candidate will contribute to the development of ultrafast
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/or R. Develop, maintain, and implement custom scripts for data acquisition, processing, and analysis as well as machine learning algorithms and techniques to classify and predict outcomes based
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | 18 days ago
include (but are not limited to): Develop algorithms to characterize aerosol speciation from LIDAR fluorescence signals Develop machine learning emulators to represent forward operators for polarimeter-only
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state-of-the-art high-performance computing. Key Research Areas: AI for Science: Research and development of large-scale AI models for science, focusing on pre-training, instruction-based fine-tuning, and