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www.hr.pitt.edu/benefits. Duties: The instructor will assist with the oversight of the program’s courses and machine shop, and will teach conventional machining, including the operations of mills, lathes, grinders
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-leading machine learning experts at CMU. We encourage candidates to pursue extramural research grants independently, supporting their evolution into independent investigators through mentorship on grant
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large acoustic data sets collected by the lab; (iii) apply machine learning models to identify species present in audio data; (iv) use statistical models to extend data to estimates of species occupancy
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to learn the preferred statistical programming languages will be considered. Candidates may be asked to demonstrate computer programming proficiency through a practical exercise. Equivalent combination
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of Medicine is dedicated to advancing biomedical research and clinical care through innovative technologies. Our research spans genomic and proteomic data mining, machine learning, and bio-surveillance. As part
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to complete projects, gain experience, and develop professional skills that are applicable to roles within the field of research administration. The ideal candidate should be growth-minded, thrive in a learning
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of students. Our classes span the full spectrum of computer science and data science such as machine learning, systems, artificial intelligence, algorithms, databases, data analytics, computer vision, graphics
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of students. Our classes span the full spectrum of computer science and data science such as machine learning, systems, artificial intelligence, algorithms, databases, data analytics, computer vision, graphics
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processes, advanced machine vision algorithms, bioinformatics, and deep learning models for image and data processing, ideally in a comparable field or industry segment. Develop and implement both algorithmic
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in the biological sciences domain, and end-to-end experience with data science projects that leverage data mining, statistics, predictive modeling, generative AI, and/or machine learning algorithms