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-level rise and intensified storm activity driven by climate change. In many of these regions, engineered coastal protection infrastructure is limited, creating an urgent need for cost-effective and
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vivo (e.g., brain organoids) and in vivo (e.g., mice) experimental models. Our Group collaborates with colleagues based in two international consortia: CHARGE and ENIGMA. Our research takes place in both
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of ecological data and sustainability issues - Materials Science: AI-driven discovery and design of new materials Applicants should have (i) a Ph.D. in Computer Science, Computer Engineering, Electrical
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modelling and dynamic material planning and production and scheduling into an actionable decision-support toolkit; Embedding explainable AI to ensure planners and engineers understand, trust, and use
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to information. We work on search engines, on recommender systems, and on conversational assistants. There is a heavy emphasis on data-driven methods, for understanding content, for analyzing and predicting user
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an approachable and highly experienced research team. You will explore cutting-edge topics in specification-driven development, large language models, and AI-assisted software engineering. Your job As a PhD
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approaches. Machine Learning in Geotechnical Engineering: Utilising data-driven approaches to model and predict soil-structure interactions or other complex geotechnical problems. Reliability-Based
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(https://aihub.osu.edu ). The AI(X) Hub at The Ohio State University is a university-wide initiative to accelerate research, innovation, and education in artificial intelligence. It spans 15 colleges and
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and explainable AI models for Medical Imaging and aid Medical Diagnosis, Digital Health and Well-Being with AI. Introducing virtual twin-driven AI models for medical diagnosis of a variety of diseases
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motivated Research Engineer skilled in computational modeling and basic laboratory techniques to join our dynamic laboratory team. We are working on multiple tissue engineering projects for improving human