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Requirements Ph.D. in Mechanical Engineering, Electrical Engineering, or a related field Desired Qualifications Strong background in: Control theory (e.g., robust control, adaptive control, LPV systems
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mobile robotic control systems Collaborating on development and implement estimation algorithms for time and frequency estimation Integrate estimation and control algorithms into various experimental
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power control of second-life batteries. The successful candidate will work with Dr. Huazhen Fang and other collaborators. The research aims to advance the technologies needed to repurpose retired electric
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, religion, sex, sexual orientation, gender identity, national origin, citizenship, age, disability or protected veteran status. Required Degree Doctorate -Mechanical, Civil Engineering, Material Science
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-engineering skills, including experience with Fortran and Python and, preferably, C or C++, APIs, dynamic-link libraries, version control, Linux environments, and high-performance computing. The candidate
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data; conduct quality assurance and quality control; and analyze and interpret monitoring datasets. Additional responsibilities may include field sample collection, development of monitoring
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troubleshooting. Strong Python skills are required for workflow automation, data processing, testing, and model orchestration. Familiarity with C or C++, APIs, dynamic-link libraries, version control, Linux