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. You will be part of an interdisciplinary team bridging the gap between machine learning and materials science to develop next-generation computing hardware based on nanoscale magnetic systems
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-funded project. You will be part of an interdisciplinary team bridging the gap between machine learning and materials science to develop next-generation computing hardware based on nanoscale magnetic
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, and — drawing on what the team learns from off-the-shelf hardware — contribute to the development of custom mobile platforms purpose-built for construction assembly tasks. The Research Assistant is
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-learning models on resource-constrained hardware Candidates should also have experience or strong interest in Edge AI, TinyML, embedded machine learning, neuromorphic computing, or signal processing. We
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electric aircraft DC bus. Working with an industry partner, you will take the framework from analytical concept through to a hardware-validated methodology. You will be responsible for: Benchmark framework
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-of-the-art technology to support the project. Knowledge in Artificial Intelligence (AI)/Machine Learning algorithms and hardware is preferred Preferred Qualifications: Significant experience in one or more of
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, email, general purpose software and hardware. Install and configure computer systems running Windows, MacOS, or IOS and peripherals. Provide basic support for personal devices to configure University
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, simulation, and experimental validation of energy systems is expected. Preferred Qualifications: Experience with artificial intelligence and machine learning for energy systems, including data-driven modeling
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visiting artist events across specialized audio hardware, software, Dante, and video systems. Maintaining facility flow by inspecting equipment, monitoring schedules, managing gear check-outs, and
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science, artificial intelligence, or a closely related field. A strong background in machine learning and deep learning. A good understanding of Transformer architectures and modern AI models. Good programming skills