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ground and space-based astronomy, engineering and technology development, and education. The research objectives of the CfA are carried out primarily with the support of government, Smithsonian Institution
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prototyping, fabrication experiments, development of experimental setups, material testing, design modelling and optimisation, and the preparation of workflows interfacing with robotic and construction
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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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including data preparation, feature engineering, model training, validation, deployment, and monitoring in production. Natural Language Processing: Advanced knowledge of NLP techniques and large language
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is generated and integrated in complex multicellular systems remains limited. Progress in the field requires the development of engineered multicellular models as mechanical reference systems, new
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rupture, which is one cause of a stroke and thus the prediction of plaque rupture is very relevant. The steps in the development of surrogate models are building data-driven models from medical imaging
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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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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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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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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