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Field
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physics. Development of new artificial-intelligence and machine-learning techniques for high-energy and nuclear physics. Close interaction with our collaborators in the EIC and the BNL theory group will be
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 2 months ago
://doi.org/10.3389/fenvs.2025.1473890 Madani, N., et al., & Miller, C. E. (2024). A machine learning approach to produce a continuous solar-induced chlorophyll fluorescence dataset for understanding Arctic
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learning and image processing techniques for estimating the onset of arterial disease. 2)Implementation of denoisers as priors for the solution of inverse problems. Implement code in PyTorch and other
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, curriculum development, or arts-based pedagogies. Familiarity with participatory data visualisation, creative learning models, and the role of the arts in social change. Knowledge of educational barriers
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qualifications include: Experience with radio interferometric observing, data processing, and imaging. Experience with modern machine learning / deep learning techniques and software packages. Experience with time
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difficult and the creation of more intelligent process control strategies and innovative methods of tracking reliability can be achieved with expert informed machine learning techniques, which offer more
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high intellectual merit and the willingness to use machine learning and/or AI techniques. Essential Duties and Responsibilities: Successful candidates are expected to develop an impactful research
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have: Expertise in processing optical remote sensing data over large areas and using machine learning models for predicting ecological metrics Strong background in remote sensing in hyperspectral imaging
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longitudinal modeling, machine learning methods, subgroup analysis, or other advanced modeling techniques is highly desirable. Software Proficiency: Experience with neuroimaging tools such as AFNI, SPM, FSL
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Associate will contribute to ongoing projects and have the opportunity to develop independent research aligned with the aims of the ADN lab. Current work focuses on machine learning and multivariate decoding