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, Computational Linguistics, Machine learning, Computer Engineering or related fields Preferred Qualifications: ● Strong experience implementing and training deep learning models in PyTorch, with attention
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of hydrological connectivity of soil moisture using gridded soil moisture data sets and data-driven approaches (e.g., complex network methods) Develop models to predict gatekeeper locations and their relationship
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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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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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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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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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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