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to join our cutting-edge team, working on the development of advanced AI/ML algorithms for battery management systems (BMS) in electric mobility and micro mobility applications. The primary focus will be
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material intralogistics handling alternatives such as trolley-assist systems, battery-electric trucks, and in-pit crushing & conveying (IPCC), combining life-cycle analysis, and dynamic simulation
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algorithms for battery management systems (BMS) in electric mobility and micro mobility applications. The primary focus will be on creating and optimizing state-of-charge (SOC) and state-of-health (SOH
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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
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. Electrochemical Impedance Spectroscopy (EIS) to analyze charge transfer resistance. Cyclic Voltammetry (CV) and Galvanostatic Intermittent Titration Technique (GITT) for diffusion coefficient determination
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Spectroscopy (EIS) to analyze charge transfer resistance. Cyclic Voltammetry (CV) and Galvanostatic Intermittent Titration Technique (GITT) for diffusion coefficient determination. Degradation Studies
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: PhD in solar energy, electrical engineering, or environmental sciences. Proficiency in PV systems, instrumentation, and performance measurement. Experience in processing environmental data (Python, R
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component. The battery management system (BMS) unit is not just a component, but a crucial element tasked with monitoring each cell of the battery and running algorithms to calculate state of charge (SoC
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such as air quality monitoring, water leak detection and energy monitoring of electric vehicle batteries. The candidate will need to master sensor technologies, embedded systems, and communication protocols
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and compare material intralogistics handling alternatives—such as in-pit crushing & conveying, trolley-assist systems, and battery-electric trucks—in strip mining operations. This research is part of