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. Preferred Qualifications • Experience with deep learning architectures applied to geophysical or environmental data. • Familiarity with physics-informed machine learning or hybrid modeling approaches
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candidate with expertise in the following four areas: (1) working with large-scale digital trace data; (2) building and running natural language processing and machine learning workflows; (3) experimental
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healthy and tumor-bearing animals using machine learning and AI approaches; and (3) integration of PBPK and QSAR models with AI methods to develop AI-assisted computational approaches to support decision
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imagery). Experience in building data models using Python or other statistical and/or mathematical programming packages. Proficiency in developing machine learning algorithms to analyze spatial-temporal
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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 17 hours ago
combination of observational data, machine learning techniques, and cosmological simulations. The group is actively involved in multiple JWST Guaranteed Time Observation (GTO) and General Observer (GO) programs
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strategies. The candidate will join the Machine Intelligence Group for the Betterment of Health and the Environment (MIGHTE) led by Prof. Mauricio Santillana. MINIMUM QUALIFICATIONS PhD in a quantitative field
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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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PhD in Data Science, Computational Social Science, Computer Science, or Information Science. The position requires experience with at least one of the following: Data Science, Machine Learning
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/Python coding, next-generation sequencing data interpretation, large-scale data integration, and machine learning. Science: strengthen the ability to formulate hypotheses, design aims to test the
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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