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Laboratory (ORNL) is seeking several qualified applicants for postdoctoral positions related to Computational Methods for Data Reduction. Topics include data compression and reconstruction, data movement
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scientists. The emphasis will be on enabling high-fidelity image reconstructions from sparse and noisy data, leveraging state-of-the-art methods in compressed sensing, optimization, and machine learning
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, including filament extrusion, compression molding, injection molding, and fused deposition modeling. This position requires the preparation of ISO/ASTM test specimens for mechanical (tensile and flexural
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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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. You have experience in matrix algorithms, data compression, parallel computing, optimization of advanced applications on parallel and distributed systems. An excellent scientific track record proven
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through our online application portal before November 4th, 2025. Please do not submit compressed files. We will not consider applications sent via email or postal services. We will get in touch with you
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, biologists, and data scientists. The emphasis will be on enabling high-fidelity image reconstructions from sparse and noisy data, leveraging state-of-the-art methods in compressed sensing, optimization, and
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Post-Doctoral Associate in the Center for Translational Medical Devices (CENTMED) - Dr. Panče Naumov
, compression molding, injection molding, and fused deposition modeling. This position requires the preparation of ISO/ASTM test specimens for mechanical (tensile and flexural) testing of polymers and composites
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focus lies on a complementary approach of compression testing and high-pressure torsion deformation. The experiments are performed on hydrogen pre-charged, nanostructured metals and require analysis
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-agent AI, cooperative vision, and compression protocols so fleets of intelligent machines can perceive the world—robustly, efficiently, and in a trustworthy manner—even when individual sensors fail