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their thermophysical properties. Proficiency in computational fluid dynamics (CFD) simulation of thermal energy storage systems, enabling the modeling and analysis of heat transfer, fluid flow, and thermodynamic
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of thermal energy storage systems, enabling the modeling and analysis of heat transfer, fluid flow, and thermodynamic behavior within the storage system to optimize design and performance. Demonstrated
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applying AI/ML models to energy systems or similar applications. Proficiency in machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn). Hands-on expertise in programming languages such as
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in real time. The candidate will contribute to modeling interactions between physical and digital infrastructures to enhance energy management, transportation networks, and urban sustainability. Key
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innovative comminution strategies. The successful candidate will play a leading role in advancing research on autogenous grinding as a low-energy alternative for liberating indurated phosphates stored in
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. without vegetation. Studying dust deposition and its impact on energy performance. Analyzing meteorological, soil, and energy production data. Contributing to the modeling of PV performance under local
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secondary sources (e.g., slags and industrial wastes). Developing purification pathways to produce battery-grade vanadium for energy storage applications. Applying thermodynamic modeling tools (CALPHAD
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Intelligence, Machine Learning, Data Science, Electrical Engineering, or a related field. Strong experience in developing and applying AI/ML models to energy systems or similar applications. Proficiency in
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and uncertainty analyses to quantify trade-offs under varying operating conditions. Simulation Build a dynamic simulation of mine haulage energy flows and storage. Validate models in collaboration with
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: transport properties and selectivity. Monitor and analyze system performance: current efficiency, ion separation rates, energy consumption, etc. Model ion transport and system behavior under different