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Field
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works to interpret genomes and distill the immensely complex networks that form the foundation of human biology and disease, through accurate machine learning models. Current areas of interest include
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the following training will be considered PhD in computer science, machine learning, AI or related computational field, or, Ph.D. in a health-related discipline with experience in experimental science, devices
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, performing scholarship in the Center’s core areas; give one public lecture on the topic of their research and teach a 2-1 load with the candidate expected to teach one (or two if desired) of the Center’s core
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Theory, Computing Theory o Programming Language, AI Theory or Machine Learning o Classical and Quantum Algorithm for Computational Quantum Many-body Theory o Theory and Computation of Correlated Quantum
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from sensors or other continuous data sources. Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages
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, Algorithm, Extremal Graph Theory, Computing Theory o Programming Language, AI Theory or Machine Learning o Classical and Quantum Algorithm for Computational Quantum Many-body Theory o Theory and Computation
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to work within a team environment. Adaptability to a fast-paced, dynamic environment. Multitasking essential. To learn more and apply, please visit: https://careers.dana-farber.org
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machine learning, coupled fire behavior/fire atmosphere modeling, air quality modeling, and system evaluation. Depending on their skills and interests, they can participate in various aspects of the project
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and October 2025. Essential Functions Develop/implement methods using hydrological models, remote sensing, data assimilation, and machine learning techniques to simulate global river discharge Present
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interested in applicants who use advanced quantitative methods, including computational modeling, machine learning, and/or analyzing structural and functional neuroimaging data. Specific activities may include