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(but are not limited to) Computer Science, statistics, mathematics, automation, informatics, and Engineering. Experience in deep learning, machine learning and medical imaging processing Programming
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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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The Data Science Learning Division at Argonne National Laboratory is seeking a postdoctoral researcher to conduct cutting-edge computational and systems biology research. The primary focus
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 3 hours ago
. Description: This project aims to develop a next-generation wildfire risk assessment platform that tightly integrates Earth Observation (EO) data, deep learning, and dynamic fire behavior modeling
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techniques such as yeast display and deep mutational scanning, or computational candidates with experience in generative AI, reinforcement learning, or agentic AI. The lab is supported by world-class
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Details Posted: Unknown Location: Salary: Summary: Summary here. Details Posted: 27-Feb-26 Location: Brooklyn, NY Categories: Academic/Faculty Internal Number: 164090 POSTDOCTORAL ASSOCIATE New York
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 6 days ago
position will include, but is not limited to, multimodal+embodied semantics, human-like language generation and Q&A/dialogue, and interpretable and generalizable deep learning. The duties of the postdoctoral
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deep learning including data collection, architecture development, model training, and validation Interest in software development, with particular emphasis on the Python programming language and
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Modeling. Machine Learning Interatomic Potential (MLIP) accelerated simulations. Demonstrated ability of coding in Fortran, Shell, or Python with development experiences. Deep knowledge in excited states and
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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