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into operational use cases. Prepare data collection frameworks and work on fish health monitoring datasets for machine learning training and benchmarking. Support the development of translational “lab-on-farm
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exciting project that will develop new approaches to handle missing data in statistical analyses based on machine learning methods. The Research Fellow will be based in the Department of Medical Statistics
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domain in the design of deep learning algorithms for cardiovascular disease detection. 4. REQUIRED PROFILE: Admission requirements: Master's degree in Biomedical Engineering, Computer Engineering
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algorithm development, modeling machine learning, and scientific simulation ▪ Ability to work well in an interdisciplinary environment, and to collaborate with experimentalists ▪ Strong oral and written
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Responsibilities Development of new machine learning modeling approaches Development of new advanced control and optimization algorithms Optimization of carbon capture process operation Provide regular project
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optimization, with applications in chemical and pharmaceutical manufacturing; data-driven modelling and machine learning applications in process industries; advanced process control (APC); model predictive
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of Physics, Computer Science, Machine Learning, and Statistics, and enjoys close relationships with the University of Pittsburgh’s Department of Physics and Astronomy. Carnegie Mellon’s physics faculty hold
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staff in exhibit preparation, including object research, selection, mounting, and installation as needed. Fellowship Learning Goals Learn and follow recognized standards and policies for collection
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programming language Experience with statistical inference or machine learning methods (e.g. ABC, Bayesian modelling) A proven publication record with at least one first author publication in a peer-reviewed
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health records (EHR), waveforms from bedside monitors, radiology images and wearable sensors. This position offers a unique opportunity to work closely with clinicians on applications of machine learning