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that you are eligible for, including health insurance, retirement plans, and paid time off. To access this tool and learn more about the total value of your benefits, please click on the following link
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Associate Research Scientist / Post-Doctoral Associate in the Division of Science (Computer Science)
to develop applied research skills in machine learning, interact with an international network of collaborators, and gain post-doctoral research experience. The ideal candidate is self-motivated and can work
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responsibilities, and other special ad hoc assignments as requested at the administrative unit, college/division, institute, and/or university level. Recognizing that collaboration and participation in teams
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communication skills, including the ability to translate statistical results for clinical audiences and to teach non-statisticians. Brief Description of Duties: The Postdoctoral Associate will work under the
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. We expect successful applicants to work towards improving the collaborations and connections among the different areas. We invite applicants to visit our website to learn more about current research
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projects within the CUS related to urban sustainability, environmental monitoring, and urban resilience. Key Duties • Design and implement machine learning and deep learning models for hydrological
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imaging specialist – experience in quantitative image analysis, scattering modeling, signal processing, machine learning, or neural-network-based data interpretation. The project is closely connected
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representations • Research on multimodal representation learning for fusing heterogeneous clinical data (imaging, pathology, genomics, clinical text) into unified vector representations • Design and evaluation
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Current Employees: If you are a current Staff, Faculty or Temporary employee at the University of Miami, please click here to log in to Workday to use the internal application process. To learn how
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collaborate closely with Professor Colin Meyer at Dartmouth College to develop new models of polynya dynamics, efficient algorithms for inversion of surface signatures, and deeper understanding of controls