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on an optical table and integrating novel optics to microscopy are a requirement for this position. Knowledge in computational imaging, mathematical modeling, strong computer programming skills in MATLAB and/or
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, with a focus on building multimodal AI models to predict dental caries progression. The successful candidate will work on developing deep learning and computer vision models using longitudinal dental
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to student learning, research, health care, and service is seeking applicants for a postdoctoral fellow position in the Department of Pediatrics, Division of Medical Genetics. The candidate will join a dynamic
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physics. Development of new artificial-intelligence and machine-learning techniques for high-energy and nuclear physics. Close interaction with our collaborators in the EIC and the BNL theory group will be
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structured and unstructured data, such as text, images, and videos. Additionally, there are individual machines available, each with high-performance GPU cards for enhanced processing capabilities. Various
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Investigator, the Postdoctoral Associate will conduct specialized cardiovascular research techniques and procedures with focus on artificial intelligence and machine learning platforms that will promote
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qualifications include: Experience with radio interferometric observing, data processing, and imaging. Experience with modern machine learning / deep learning techniques and software packages. Experience with time
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high intellectual merit and the willingness to use machine learning and/or AI techniques. Essential Duties and Responsibilities: Successful candidates are expected to develop an impactful research
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significant mathematical and computational background to learn and develop new AI methods. While demonstrated experience in computer vision and deep learning for pathology or other biomedical image analysis is
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Associate will contribute to ongoing projects and have the opportunity to develop independent research aligned with the aims of the ADN lab. Current work focuses on machine learning and multivariate decoding