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The University of Chicago Academic Jobs Postdoctoral Researcher at the Rank of Instructor Committee on Conceptual and Historical Studies of Science, Social Sciences Division Position Type: Faculty
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related field by the start date, with a strong publication record in computer vision, multimodal learning, or vision–language models. We require hands-on expertise with transformer architectures (e.g., ViT
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courses (approximately two courses per year). Additionally, they will assess the effectiveness of these strategies on student engagement, learning outcomes and sense of belonging. The Postdoctoral Associate
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geospatial modeling approaches to analyze urban environmental processes. • Apply machine learning and geospatial data analysis techniques to large Earth Observation datasets. • Produce high-quality
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Position Information Posting Number F00741P Position Title Postdoctoral Research Associate Department Industrial Engineering Location Arlington Job Family Faculty Position Status Full-time Rank Non
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. • Contribute to interdisciplinary research projects within the CUS related to urban sustainability, environmental monitoring, and urban resilience. Key Duties • Design and implement machine learning and deep
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for machine learning and AI workflows In addition to computational resources, the postdoctoral researcher will have access to dedicated wet-lab facilities at the University of Chicago and Argonne National
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) Assist in 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
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the leadership of Principal Investigator Dr Andrew Siemion. Listen's interdisciplinary research has synergies with many of the department's research priorities, including exoplanet studies, machine learning
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Postdoctoral Positions for Computational Genomics, Cancer Genetics, and Translational Cancer Biology
mechanism-driven AI and agentic AI frameworks (iGenSig-AI, G2K) that integrate biological knowledge with cutting-edge machine learning to transform omics data into actionable therapeutic insights