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, automated design-space exploration, and cross-technology benchmarking, providing new insights into the co-design of learning algorithms, memory technologies, and neuromorphic hardware architectures for future
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data, code, models, and documentation in a way that supports findability, accessibility, interoperability, and reusability. A willingness to learn new algorithms, tools, and developments in AI, including
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given to those with expertise in two or more of the following areas relating to the position: AOCS subsystem architecture, design, testing and verification, including control algorithm design and tuning
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