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for Transportation (VITA ) is looking for a postdoctoral researcher in the area of Generative AI. VITA research interests lie at the intersection of Computer Vision, Machine Learning (Deep Learning), and Human-Robot
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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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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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sensors, RGB/IR cameras, video systems, insect traps, and other devices to build predictive, AI- and machine-learning based models for monitoring grain quality and detecting deterioration due to mold
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public health would be highly desirable. Knowledge and skills in mathematics, biostatistics, advanced statistical techniques in clinical research, database management, and machine learning/artificial
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and predict how the immune system responds to interventions. This tight integration of advanced machine learning and experimental immunology allows us to tackle fundamental biological questions with
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echocardiography dataset called CAIFE consisting of both healthy and abnormal fetal heart scans. You will be responsible for the design and testing of original machine-learning based methods for fetal heart
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Associate Research Scientist / Post-Doctoral Associate in the Division of Science (Computer Science)
, seeks a Post-Doctoral Associate or a Research Associate to join a lab focused on applied machine learning. The successful applicant will participate in research involving human computation, knowledge
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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