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Methods and Dynamics (MMD) Group at Oak Ridge National Laboratory (ORNL) is seeking several qualified applicants for postdoctoral positions related to Computational Methods for Data Reduction. Topics
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Requisition Id 15997 Overview: We are seeking a postdoctoral researcher who will focus on atomistic simulation and data science approaches. This position resides in the Chemical Transformations
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to Computational Methods for Data Reduction. Topics include data compression and reconstruction, data movement, data assimilation, surrogate model design, and machine learning algorithms. The position comes with a
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postdoctoral research associate to advance the state of scientific AI by addressing cross-cutting challenges in data readiness for AI to enable scalable, reproducible AI workflows on leadership-class systems
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array of capabilities in nuclear nonproliferation, data analytics, cybersecurity, cyber-physical resiliency, geospatial science, and high-performance computing, our organization seeks to produce world
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materials. In this role, you will develop and apply methods that integrate physics‑guided image correction with intelligent (AI/ML‑enabled) data‑acquisition strategies. Key objectives include (1) implementing
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, United States of America [map ] Appl Deadline: (posted 2025/10/03 05:00 AM UnitedKingdomTime, listed until 2026/04/04 04:59 AM UnitedKingdomTime) Position Description: Apply Position Description Overview: The Data and AI
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analysis of large-scale 2D/3D scientific data. This position resides in the Data Visualization Group in the Data and AI Systems Section, Computer Science and Mathematics Division, Computing and Computational
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length scales Develop machine learning algorithms to support process optimization, predictive modeling, and intelligent manufacturing control Integrate simulation tools with in-situ sensor data from
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Postdoctoral Research Associate who will focus on creating innovative artificial intelligence algorithms for the trusted visualization of large-scale 3D scientific data. This position resides in the Data