Researcher

Description

Research, develop, and improve battery electric vehicle (BEV) next-generation lithium-ion batteries (Li-ion batteries or LIBs) and alternative sodium and phosphate-based materials, including performance and cycle life of positive and negative electrodes. Perform and execute research to better understand chemical failure mechanisms of future BEV LIBs. Research, design, and conduct laboratory experiments to study and develop cathode materials for LIB applications. Investigate, research and develop Machine Learning algorithm to determine battery state estimation, State of Charge (SOC) and State of Health (SOH) estimation for LIBs. Predict specific capacity of LIBs using Artificial Intelligence and determine dynamic resistance of LIBs. Develop robust solution frameworks for modeling problems, based on Artificial Intelligence (AI) and nonlinear Machine Learning (ML) models including Gradient Boosting (GB), Bagging, Recurrent Neural Networks (RNN), Autoencoders (AE), and Deep Learning (DL), manipulating problem-specific information to tailor algorithms with customized regularization, model architecture and objective function. Implement proposed methods in parallelized and modular Python programs

Additional Description

PhD in Electrical Engineering, Chemical Engineering, Computer Engineering, Materials Science, Chemistry, or related field, and six (6) months' experience as an Engineer, Researcher, Graduate Research Assistant, or related role.  Must also have experience with: researching and developing battery electric vehicle (BEV) next-generation lithium-ion batteries (LIBs) or alternative sodium-ion batteries (NIBs) including performance and cycle life.

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