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position in biomedical informatics is available at Harvard Medical School to work at the intersection of advanced machine learning and large-scale biomedical data. The selected fellow will join a dynamic
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) CV One (1) writing sample or representative work/publication Contact information for three (3) references Supervisors: Dr. Devon Noonan PhD, FNP-BC, Nursing Dr. Jessica Sperling, PhD, Social Science
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sociology. Strong quantitative skills and experience with large-scale data analysis required. Computer Science/HCI: PhD in Computer Science, Human-Computer Interaction, Information Science, or related
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integration analytics, machine learning, and/or AI. In addition to carrying out research, the successful candidate will be expected to apply for fellowship funding, contribute to the writing of grants and
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duties as assigned. REQUIREMENTS: REQUIRED: PhD in in computer vision, machine learning, artificial intelligence, or a closely related field. Strong programming skills. Strong background in machine
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, United States of America [map ] Subject Areas: Electrical and Computer Engineering / artificial intelligence , Artificial Intelligence and Machine Learning (AI/ML) Starting Date: 2026/01/01 Salary Range: $62,232-$80,000
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experience in at least three of the following: developing watershed model input datasets, geospatial analysis, applying large-scale hydrologic models, artificial intelligence and machine learning, computer
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managing large multimodal datasets, as well as contributing to analytical studies related to machine learning, clinical decision rules, and time-to-intervention evaluations. Responsibilities include curating
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The postdoctoral fellow will lead and co-lead projects that combine computational modeling, machine learning, and EEG to answer questions about scene understanding and neural representation. The fellow will work
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, New York 14850, United States of America [map ] Subject Areas: Data Science / Statistics , Applied Mathematics , Artificial Intelligence , Bayesian Statistics , Big Data , Scientific Machine Learning