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researcher specializing in Li-ion cathode materials to join our research team. This position focuses on the development, synthesis, characterization, and optimization of advanced cathode materials for next
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dedicated to advancements in sustainable energy through innovative research and training. Our focus lies in the design, development, and optimization of inorganic materials for various sustainable energy
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on creating and optimizing state-of-charge (SOC) and state-of-health (SOH) prediction models to ensure the safety, efficiency, and longevity of lithium iron phosphate (LFP) batteries. Key Responsibilities
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. The successful candidate will develop advanced machine learning (ML) models to automate and optimize retrosynthetic analysis, facilitating the discovery of efficient and sustainable synthetic routes for complex
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in Statistial models for optimizing the chemical process Skills in elemental & structural analysis using various existing techniques. Knowledge in advanced structural characterizations will be valued
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of strategic metals and the development of fresh Lithium-ion Batteries & Supercapacitors Experience in Statistial models for optimizing the chemical process Skills in elemental & structural analysis using
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, analyze, and optimize new algorithms and system architectures for secure communication. Bridge theory and practice by developing simulation frameworks and/or contributing to experimental testbeds. Publish
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multiplex and multilayer networks alongside with the observed links in order to predict or reconstruct the missing links. The first step is to explore different optimization methods using low rank tensor
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associations, and the surface properties of the minerals. In general, a mineral cannot be floated even if the processing conditions are optimized if it is locked (encapsulated). To improve flotation recovery
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surfaces using surface chemistry and interfacial analysis techniques. Contribute to the optimization of flotation process parameters based on experimental results. Work closely with a multidisciplinary team