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Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | 3 months ago
/Administrative Internal Number: 25848 POSTDOCTORAL ASSOCIATE, Jameel Clinic for Machine Learning in Health, seeks to support a collaborative research program between the laboratories of Professor Omer Yilmaz, Koch
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stimulation (TMS), and transcranial direct current stimulation (tDCS) through interdisciplinary collaborations. Behavioral measurements and physiological recordings, including facilities for computer-based
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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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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 1 month ago
Posting Description POSTDOCTORAL ASSOCIATE, Jameel Clinic for Machine Learning in Health, seeks to support a collaborative research program between the laboratories of Professor Ömer Yilmaz, Koch
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. Ideal candidates will have demonstrably strong research skills, evidenced by multiple publications in top-tier machine learning or artificial intelligence conferences and/or leading scientific journals
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to have published in leading machine learning conferences or similar venues. One or two PDRAs will be recruited to work within one of, or across, the four research themes: Learning with Structured
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. Knowledge of survival analysis. Knowledge of machine learning methods. Other Requirements Application materials must include: Curriculum Vitae (CV) Statement of research interests Names of three professional
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-science and signal-analysis tasks, including processing experimental signals, integrating datasets, developing machine-learning models, and mapping measured fuel properties to SAF performance. For the post
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strategies within it. You will combine archaeological and landscape data (maps, satellite imagery, archaeological datasets) with machine learning approaches to build a system that highlights promising
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are essential. In addition to applicants’ prior accomplishments, we would like to learn what motivates them, how they approach unfamiliar problems, and what they hope to contribute to, and learn from