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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 1 month ago
connections between wet and dry lab, bringing together computational biologists building new methods at the interface of structural biology and machine learning and experimentalists studying how proteins
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development About You You will hold a Ph.D/D.Phil in a quantitative or theoretical discipline (e.g. machine learning, computer science, mathematics, statistics, physics, theoretical neuroscience or a closely
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, integrates, and evaluates innovative solutions in autonomous systems, including artificial intelligence (AI), machine learning, robotics, control systems, sensing technologies, and related disciplines
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germplasm characterization, including calculation of sequence based genomic prediction and genomic selection values. To learn more about the USDA ARS NPGS and Plant Genetic Resources, visit our webpages
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of generative AI tools, use of large language models, machine learning, and ethical frameworks for AI implementation. Ability to apply AI to interdisciplinary research or developing AI models
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independent scientific reasoning and problem-solving Learn and apply new experimental technologies and methodologies Contribute to high-impact research advancing lung biology and disease understanding Choose
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(Large Language Models, Convolutional Neural Networks, Machine Learning) for analysis and classification of data.
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Intelligence and Machine Learning. Join a team of scientists at the leading macromolecular crystallography beamlines, the crystallization laboratory, the computing center and contribute to science projects
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-scale human datasets. You will: - Build and apply machine learning and deep learning models to multi-scale (cells, brains, patients), multi-modal (omics, biosensor data, vision, electronic health data
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Researcher will work in Professor Benjamin Peherstorfer’s group (https://cims.nyu.edu/~pehersto/ ) on scientific machine learning at the Courant Institute of Mathematical Sciences where they will help