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Field
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original research on clandestine printing networks using computational tools Contribute to publications in both AI and humanities venues (machine learning conferences and book history journals) Contribute
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, creative start-ups, big data, big ambitions, hands-on learning, and a whole lot of robots, CMU doesn’t imagine the future, we invent it. If you’re passionate about joining a community that challenges the
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novel machine learning models—including Physics-Informed Neural Networks (PINNs), variational autoencoders, and geometric deep learning—to fuse multimodal data from diverse experimental probes like Bragg
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development, and publication in peer-reviewed venues. Strong background in machine learning, with research experience in deep learning, foundation models, or related areas. Solid programming ability in Python
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participant outcomes. The project will use a variety of approaches, including human perceptual experiments, machine learning, digital signal processing, and computational models of hearing. UConn has a vibrant
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regional leadership in biostatistics, genomics, biomedical informatics, artificial intelligence and health data science. The Postdoctoral Associate will conduct research in statistical machine learning and
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combinations of theoretical physics, mathematics, and machine learning. While machine experience is not a strict requirement for the job, we anticipate that the strongest candidates will have clear research
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Biology with both high-throughput experimental (proteomics and genomics) and integrative computational (network analysis and machine learning) methodologies, aiming to understand gene functions and their
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a team-oriented research environment. PREFERRED QUALIFICATIONS Prior experience in one or more of the following areas: longitudinal data analysis, survival analysis, spatial methods, machine learning
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multiple symbolic fields, including official political discourse, academic debates and selected cultural outputs. Methodologically, it adopts a mixed-methods approach, combining machine-learning–enhanced