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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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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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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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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 4 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