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generative AI models to establish structure-property relationships for materials discovery. This individual will be expected to collaborate closely with the project team and coordinate efforts with
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. The researcher is expected to have (i) strong machine learning skills to improve model performance and robustness, and (ii) exemplary passion and motivation to pursue multidisciplinary research at the intersection
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. We are seeking a Postdoctoral Researcher to join the team and make significant contributions to the field. The researcher is expected to have (i) strong machine learning skills to improve model
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develop a glaucoma implant. This project entails computational modeling, implant design and manufacturing, optimization, and preclinical experiments using an animal model. The candidate will be working
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the design, testing, and analysis of resilient and sustainable geotechnical systems using centrifuge modeling. The work will bridge advanced experimental testing with complementary numerical simulation
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, cardiovascular, and neurologic diseases. These projects entail computational modelling, device design and manufacturing, optimization of chemical, mechanical, and electrical characteristics, and preclinical
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, cybersecurity, artificial intelligence, robotics, digital twins, Building Information Modeling (BIM), or related disciplines are encouraged to apply. The S.M.A.R.T. Construction Research Group conducts world
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expertise in construction engineering and management, computer science, cybersecurity, artificial intelligence, robotics, digital twins, Building Information Modeling (BIM), or related disciplines
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Post-Doctoral Associate in the Center for Interdisciplinary Data Science and Artificial Intelligence
of reinforcement learning and bandit algorithms Mathematical and algorithmic perspectives on large language models Statistical learning theory and complexity analysis Automated theorem proving and formal methods
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applicant will assist senior researchers on a diversity of projects related to the area of adaptation genomics. Our group uses a variety of model systems to understand the evolutionary processes leading