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Field
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-aware multi-modal deep learning (DL) methods. At Argonne, we are developing physics-aware DL models for scientific data analysis, autonomous experiments and instrument tuning. By incorporating prior
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 3 hours ago
-resolution NASA EO data. Subsequently, we will architect and train an ensemble of deep learning and statistical models capable of identifying key wildfire drivers and accurately predicting risk, leveraging a
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Post-Doctoral Position in Deep Learning for MRI Reconstruction at Yale University Title: Postdoctoral Associate, Yale School of Medicine Department/Division: Radiology and Biomedical Imaging
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using deep learning, computational chemistry, medicinal chemistry, chemical biology, and molecular cell biology to develop novel therapeutics to tackle complex diseases such as cancers. Successful
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novel machine learning models—including Physics-Informed Neural Networks (PINNs), variational autoencoders, and geometric deep learning—to fuse multimodal data from diverse experimental probes like Bragg
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analysis. Experience with Python and/or R; familiarity with deep learning frameworks is a plus. Demonstrated record of research productivity (publications, conference presentations). Excellent communication
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Requirements REQUIRED: Ph.D. in computer science, mechanical engineering, applied math, or related field with significant research experience in machine-learning/AI algorithm development, specifically in deep
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an exciting research laboratory with a focus on exploring molecular functionality encoded in genome data. Specifically in this project, we are looking to build deep learning models to explore the alphabet
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, Statistics, or related. Strong skills in machine learning and deep learning, with a fundamental understanding of LLMs. Proficiency inPython programmingand major ML/DL frameworks (e.g., PyTorch, TensorFlow
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and dissemination of research findings. Example Projects: Developing deep learning models to analyze spatial single-cell profiles. Using large language models (LLMs) to extract information from