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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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simulations and thermal comfort analyses by developing fast parametric algorithms and data-driven surrogate modelling approaches capable of predicting dynamic outdoor thermal comfort with high accuracy
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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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state-of-the-art magnetic imaging with advanced electron microscopy techniques. You will generate high-quality experimental datasets that form the basis for data-driven micromagnetic modelling developed
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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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in AI-driven materials discovery, machine learning applications for materials, or generative AI related to materials. The successful candidate will independently lead a project focused on developing
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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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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
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teaching staff are world leading and world building as they advance knowledge and learning. For more information on our school go to the following link - https://www.unsw.edu.au/engineering/our-schools
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8 Jan 2026 Job Information Organisation/Company NEW YORK UNIVERSITY ABU DHABI Research Field Engineering Computer science Researcher Profile Recognised Researcher (R2) First Stage Researcher (R1