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decision-making for complex infrastructure systems. This position offers an opportunity to contribute to interdisciplinary research at the intersection of civil engineering, machine learning, and systems
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machine learning—for chemical and biological applications. You will design and implement models ranging from molecular to process scales, develop model-predictive control and optimization strategies, run
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involving T cell transduction, CAR constructs, or gene editing tools. Prior experience with animal models of cancer or immune response. Willingness to learn and adapt to rapidly evolving experimental needs
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than 30hrs a week) to teach undergraduate and/or graduate course(s) in the Department of Materials Science and Engineering for Fall/Spring/Summer semester. Hours will vary, pending course(s) available
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, Automation Anywhere). Deep understanding of AI/ML fundamentals, including supervised/unsupervised learning, NLP, computer vision, and generative models. Familiarity with tools and frameworks like TensorFlow
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Provide instructional support in pre-clinical and laboratory sessions. Evaluate student work according to established rubrics. Foster a positive and interactive learning environment that promotes student
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, sensor networks, robotics, and machine learning applied to the integration of computational algorithms and physical processes; Foundational aspects and methods in Machine Learning; Applications of machine
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, with a focus on building multimodal AI models to predict dental caries progression. The successful candidate will work on developing deep learning and computer vision models using longitudinal dental
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. Jianyang Gu. This position works closely with the Imageomics team to assist with the collecting, generating, and cleaning data for advancing the Imageomics team’s computer vision and deep learning algorithms
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those important perceptual principles which influence and affect human comprehension Learn how to implement plans for graphic-symbol-based wayfinding systems through prospective guideline development