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methods and predictive control strategies for solid-state batteries using data from parallel postdoctoral projects within the initiative. The research aims to improve the understanding of how operating
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the relationship between whole-tree water use and wood cell formation in trees, using advanced growth models to predict the future of wood formation and carbon allocation in forests facing drier conditions. Within
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-related services. Maintain internal controls for each contract, which will include original contract, all correspondence, changes/deviations, amendments, clarifications, and payment schedules. Prepare and
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 26 days ago
Government investment through the National Collaborative Research Infrastructure Strategy (NCRIS). Hosted at ANU, ACCESS-NRI aims to understand and predict Australia’s climate and weather to provide
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, physical and chemical conversion strategies and engineering processes (b) understanding and controlling their underlying specific functionalities and the structure-properties relationship acting over
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on machine learning, are monitored by an integrity controller. Autonomous navigation on open roads remains a challenge, due to the uncontrolled and uncertain nature of the environment, the multiple driving
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Intelligent Control Systems RESPONSIBILITIES Develop industrial process digital twin models based on the fusion of mechanistic and data-driven approaches. Develop predictive maintenance and fault diagnosis
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healthcare, The key objective is to spearheadresearch and development to revolutionise patient care and inform public health policies. For more details, please view https://www.ntu.edu.sg/c-aim We are seeking
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 26 days ago
investment through the National Collaborative Research Infrastructure Strategy (NCRIS). Hosted at ANU, ACCESS-NRI aims to understand and predict Australia’s climate and weather to provide information that can
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(3) Machine Learning (ML) approaches that improve determinism and predictability across IBF and APPF processes. This position supports an integrated program encompassing advanced control for IBF, APPJ