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for real-time battery health monitoring and fault detection. Collaborate with embedded systems and hardware engineering teams to integrate AI models into the BMS. Optimize AI/ML pipelines for resource
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this time to find out how well we fit together In addition to exciting tasks and a collaborative working atmosphere at Jülich, we have a lot more to offer: https://go.fzj.de/benefits We welcome applications
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(as a pack consists of a large number of cells) Fault detection and predictive maintenance. As fleet owners continuously monitor the batteries in their fleet, methods are needed that can predict
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learning and physics-based approaches for battery life extension. Develop AI-based predictive maintenance techniques for early fault detection and anomaly detection in battery systems. Implement multiscale
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Current University of Arkansas System employees, including student employees and graduate assistants, need to log in to Workday via MyApps.Microsoft.com , then access Find Jobs from the Workday
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Current University of Arkansas System employees, including student employees and graduate assistants, need to log in to Workday via MyApps.Microsoft.com , then access Find Jobs from the Workday
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algorithms that support hierarchical levels of detail. The theoretical component includes convergence and error bounds for refinement, conditions for topological validity, robustness to noisy measurements, and
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and repair advanced mechanical, electrical and instrumentation faults on plant equipment. Understand how to troubleshoot equipment faults/failures and identify root causes of plant issues. Recommend a
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, including usage, performance, and error tracking. c. Troubleshoot production issues and optimize reliability, latency, and cost of AI services. d. Assist in maintaining model and endpoint lifecycles (e.g
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departmental quality assurance activities as required. Identifies risk areas and error trends for providers, procedures, facilities, and coders. Mentors and trains less experienced coders; assists with complex