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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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Expected Start Date: October 2025 Job Description We are seeking a highly motivated postdoctoral researcher with expertise in vanadium and rare-earth element (REE) recovery, thermodynamic modeling
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of the extraction and beneficiation system. This work will require an understanding of mining processes, mathematical modeling of flows and extraction decisions, and the use of machine learning algorithms to predict
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performance and enhancing resource recovery. The ideal candidate will have a strong background in mineral processing, comminution modeling, and ore characterization, and will contribute to developing
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between objects. A common way to represent a graph is to use the adjacency matrix associated with the graph. However, adjacency matrices only model networks with one kind of objects or relations between
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techniques may be employed to support the modeling of uncertainty, along with the formulation and resolution of planning problems using stochastic optimization methods. Key Responsibilities The selected
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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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) prediction models to ensure the safety, efficiency, and longevity of lithium iron phosphate (LFP) batteries. Key Responsibilities: Develop and implement machine learning algorithms for SOC and SOH estimation
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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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emissions modeling. Simulation & Optimization Framework Build and validate a dynamic simulation model of mine haulage operations. Integrate multi-objective optimization to explore trade-offs across cost