AI Pillar: AI for Science and Engineering
AI methods are revolutionizing research through being powerful tools for many steps of the research workflow, and through automating and accelerating the entire research workflow. We catalyze creative and transformative applications of AI with the potential to lead to major scientific breakthroughs; and enable a broader U-M research community to adopt AI in imagining, planning, executing, and supporting research applications across a range of science and engineering domains.
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AI in Science and Engineering postdoc program
Overview: This campus-wide program provides outstanding early-career researchers with intensive training and research experience as they ready themselves for independent research in academia and other sectors. As one of the sites in a new global network of postdoc programs focusing on AI in Science and Engineering, we support postdocs who apply AI methodology to address significant research questions. AI is defined broadly to include machine learning, robotics, Bayesian inference, and simulation. Science and engineering includes mathematical sciences, physical sciences, earth and environmental sciences, basic biological sciences, and engineering.
Who Will Benefit: Postdocs, their faculty mentors, and research collaborators.
Coordinators: HV. Jagadish (Director, MIDAS | Professor, Electrical Engineering and Computer Science), Jing Liu (Managing Director, MIDAS), William Currie (Professor and Associate Dean for Research and Engagement, SEAS)
AI in Science and Engineering for the campus
Overview: Using the postdoc training program as the core, we are developing research and training activities for campus researchers and trainees, to connect AI methodologists and domain researchers, to enable AI skills training for researchers, to build an AI in Science and Engineering research community.
Who Will Benefit: All researchers on campus who would like to apply AI methods to science and engineering research.
Coordinators: HV. Jagadish (Director, MIDAS | Professor, Electrical Engineering and Computer Science), Jing Liu (Managing Director, MIDAS)