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Field
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(completed or near completion) in Computer Science, Computer Vision, NLP, Machine Learning, Computer Graphics/Animation, HCI, or a related field. Strong background in deep generative modelling (diffusion
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the results, and communicates with the group members. Writes computer codes for the above data modalities under the guidance of the team leader. Engages in the development and testing/validation of new
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fundamental challenges in multimodal representation learning by developing novel approaches to align distinct embedding spaces from speech and sign language modalities. Sign languages encode information through
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involve the integration of: Advanced motion planning and control algorithms Multi-modal perception techniques (e.g., vision, tactile, force) Machine learning models for physical behavior prediction and
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machine learning, deep learning, data visualization, and applied analytics for multi-modal datasets. Technical proficiency with Python, R, SQL, SPSS, Tableau. Architectural and design software expertise
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machine learning. Essential Duties and Responsibilities: Develop and implement advanced reconstruction algorithms for correlated and low-dose imaging modalities. Maintain and extend Python-based software
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high-dimensional, dynamic, networked system, applying techniques from machine learning, causal inference, statistics, and algorithms. No prior biomedical training is required—just strong quantitative
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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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field. Proven experience in multi-omics data integration, omics data analysis (genomics, transcriptomics, proteomics, metabolomics, microbiome). Strong expertise in machine learning, deep learning, and
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 2 hours ago
on the principle that by integrating high-resolution Earth observation (EO) data from NASA with state-of-the-art machine learning, we can produce a more accurate, dynamic, and actionable measure of wildfire risk