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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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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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of the future Center and Protect OHIO, translating research findings into outreach-appropriate sessions, building active-learning sessions for stakeholders, learning best practices for educating and engaging
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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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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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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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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
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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