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Field
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involved in SpinQuest at Fermilab and the MUSE experiment at PSI. Our hardware program includes the ePIC Barrel Imaging Calorimeter, and instrumentation R&D such as a polarized lithium-ion source for EIC
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). Experience in neurotechnology development: hardware adaptation, electronics, device building, or related engineering skills. Background in neurodevelopmental research and/or in vivo recordings in juvenile
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background in one or more of the following areas: dynamics analysis of power systems, machine learning, cybersecurity, renewable energy, microgrids, hands-on experiences on hardware-in-the-loop projects
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++ and Python are required. Experience with control and optimization of power generation systems (e.g., wind power) is desirable but not required. Some experience with real-time control hardware is
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research within past five years. Preferred Qualifications: Ability to work independently to design and deploy methods at scale. Familiarity with hardware-software co-design, memory hierarchies (DDR, HBM
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knowledge of hardware implementations. Demonstrated ability to lead small teams and mentor students. Demonstrated strong written and verbal communication skills. Publication record in peer-reviewed journals
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hardware Experience with atomic layer deposition and process development Experience with thin film and materials characterization Strong background in computational materials science and machine learning
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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-constrained environments, including
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neuroscience through innovative AI technologies. This unique position represents an extraordinary partnership with industry-based collaborators, and will include vendor-sponsored hardware and software
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reports and presentations for project sponsor. Minimum Qualifications: PhD in Mechanical Engineering required. Experience with hardware development and instrumentation required, as well as experience with