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. ICDS seeks outstanding early‑career researchers with expertise in Artificial Intelligence and Machine Learning in the following computational and data‑driven discovery areas: Earth Science Computational
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autonomous, closed-loop (“self-driving”) laboratory workflows. The role integrates catalyst synthesis, high-throughput reactor testing, and in situ/operando characterization with machine-learning and agentic
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of lithium iron phosphate (LFP) batteries. Key Responsibilities: Develop and implement machine learning algorithms for SOC and SOH estimation. Analyze large datasets from battery systems to improve model
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for scholars who will establish an independent, nationally recognized education research program, including mentoring Ph.D. students and postdoctoral scholars and advancing the field through innovative
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microscopy, tissue culture, biochemistry, physiology, or biophysics. Enthusiasm and commitment to pursue novel research areas and develop new techniques and approaches. Team player with an interest in learning
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | about 2 months ago
. 12. S. Mo, W. Pian, and Y. Tian, "Class-incremental grouping network for continual audio-visual learning," ICCV, 2023. The postdoctoral researcher will contribute to the design, implementation, and
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Atmospheric Science, Physics or related field. Preferred Qualifications Postdoctoral or equivalent experience. Demonstrated ability to pursue external funding. Demonstrated commitment to student success via
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of measures, optimal transport, partial differential equations, and variational approximation methods, with potential applications to optimization and machine learning. The successful candidate will work in an
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Position Description UNIVERSITY OF SOUTH CAROLINA DEPARTMENT OF MATHEMATICS Postdoctoral Associates in Data Science & Machine Learning The Department of Mathematics at the University of South
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Engineering/Hardware. Postdoctoral (or industrial equivalent) research experience at lecturer level or above in Computer Engineering/Hardware. Significant experience of planning and developing teaching