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testing data Development of machine learning models for battery health assessment and remaining useful life prediction Job Requirements: PhD degree in Electrical Engineering or related subjects. Expert
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successful candidates will dedicate their efforts to the following specific research objectives: (1) Developing models for predicting the thermal runaway (TR), venting, and jet fire in a single cell with
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working with numerical weather prediction (NWP) or Earth system models such as WRF or the Unified Model. Strong preference will be given to candidates with experience in next-generation frameworks like
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successful candidate will dedicate their efforts to the following specific research objectives: 1) Developing models for predicting the thermal runaway (TR), venting, and jet fire in a single cell with
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schools in the world. For more details, please view https://www.ntu.edu.sg/mae/research . Key Responsibilities: Develop and implement high-fidelity CFD and FEA simulation workflows for modelling heat
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modes, effects, and criticality requires deep domain knowledge and careful analysis. Collecting High-Quality Sensor Data. Simulating Realistic Fault Conditions. Developing Reliable Fault Prediction Models
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acousto-structural transmission paths, developing predictive models, and producing research outputs that support practical noise mitigation solutions for the built environment industry. Key Responsibilities
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algorithms for dynamic master selection, coordinating BESS, PV, diesel generators, and other sources. Implement predictive, rule-based, or optimisation-based control strategies using MATLAB/Simulink, Python
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learning methods (e.g., predictive modelling, clustering, multivariate integration) to large-scale time series and sensor datasets. Contributing to the development of risk models and decision-support tools