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Field
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affiliated with Columbia University. Duties and Responsibilities: Construct air pollution and meteorological data prediction models using machine-learning based methods Conduct epidemiologic analyses studying
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computational chemistry, reaction network analysis, and machine learning for organometallic catalytic reactions. 2. Design of membrane-permeable macrocyclic peptide drugs via machine learning structure
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knowledge-grounded reasoning with flexible machine learning Tools that reduce manual burden while preserving traceability and clinical interpretability This position offers the opportunity to publish novel
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, and sub-daily to evolutionary time scales. One of the goals of the SCINet Initiative is to develop and apply new technologies, including AI and machine learning (ML), to help solve complex agricultural
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: To independently undertake research in machine learning. To produce research reports and/or publications as required by the funding body or for dissemination to the wider academic community. To provide guidance and
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may include, but are not limited to: Artificial Intelligence and Machine Learning in Art History Museums, Collections and AI 3D Scanning and Digital Heritage Extended Reality (VR, AR and Mixed Reality
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artificial intelligence and machine learning (AI/ML) models that predict therapeutic response, identify clinically actionable patient subgroups, and support personalized treatment strategies. Through
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Responsibilities: To independently undertake research in machine learning. To produce research reports and/or publications as required by the funding body or for dissemination to the wider academic community
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ideal for a researcher with a passion for solving complex problems at the intersection of wireless communications, edge computing, and machine learning, and who is eager to translate theoretical insights
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, and openly release evaluation code. What is Required: A recent Ph.D. (within the last 1-2 years) in Computational Biology, Bioinformatics, Machine Learning, Computer Science, Statistics, or a related