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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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Salary Guidelines, Depends on Experience Compensation Type salary at 1.0 full-time equivalency (FTE) Type of criminal background check required: Name-based criminal background check (non-security sensitive
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guidelines, Depends on Experience Compensation Type hourly rate Type of criminal background check required: Name-based criminal background check (non-security sensitive) Number of Vacancies 1 Target Hire Date
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modelling Experience with hardware-in-the-loop (HIL) emulators such as dSPACE, OPAL-RT, and Speedgoat Experience in power systems and system optimization Knowledge of battery storage technologies and battery
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-time equivalency (FTE) Type of criminal background check required: Name-based criminal background check (non-security sensitive) Number of Vacancies 4 Target Hire Date Expected End Date Contact
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at 1.0 full-time equivalency (FTE) Type of criminal background check required: Name-based criminal background check (non-security sensitive) Number of Vacancies 2 Target Hire Date Expected End Date Contact
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check (non-security sensitive) Number of Vacancies 1 Target Hire Date Expected End Date Contact Information for Candidates Tonya Fotheringham, tonyah@arizona.edu Open Date 3/31/2026 Open Until Filled Yes
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-year contract with possibility of extension A supportive, international, and interdisciplinary research environment State-of-the-art technical facilities (soft- and hardware, communication tools
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preparation and material attachment steps; document stability and reliability checks. Integrate device components with supporting hardware; perform alignment, packaging, and system bring-up. Develop basic data
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-time equivalency (FTE) Type of criminal background check required: Name-based criminal background check (non-security sensitive) Number of Vacancies 1 Target Hire Date Expected End Date Contact