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analysing multimodal deep learning models for time-specific cancer risk and time-to-event prediction by integrating imaging with longitudinal Electronic Health Record (EHR) signals. Building scalable
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science, or a related field; experience with using and building machine learning models, developing and validating computational analysis workflows, and developing circuit models is preferred; excellent
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Associate Research Scientist / Post-Doctoral Associate in the Division of Science (Computer Science)
machine learning. The successful applicant will participate in research involving human computation, knowledge discovery, machine learning, and data science. The position will provide the opportunity
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The Center for Thermal Spray Research to learn more about our department/research. * Within the predetermined research scope and methodology, conduct research experiments in the field of thermal spray and
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involved in perceptual judgments and economic decisions. Projects may involve the accuracy of judgments about probability, value, or risk; learning to forecast or categorize; the role of imprecise perception
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track record in neurobiologically mechanistic modeling (i.e., models should incorporate known neurobiology and neurophysiology, rather than relying on black-box machine learning approaches
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and fast learning ability are required. Preferred Qualifications: - Less than three years of post-graduate research experience in the fields of liver cancer, immunology, metabolic diseases, microRNA
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experience in computational and machine learning approaches for use in the quantitative measurement of human learning and its associated behaviors and motivational constructs. A strong applicant will hold Ph.D
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Investigator, Dr. Hwan Kim , in the department of Microbiology and Immunology. Please visit the Kim Laboratory to learn more about our department/research. The incumbent will conduct research and ensure
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Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | about 1 month ago
data analysis methods to study biological memory circuits and their applications to machine learning. Building on recent work from the Fiete Lab, the role focuses on identifying principles of biological