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independent R&D on an ongoing basis. Develop and grow programs in image/signal processing, applied machine learning, and artificial intelligence for applications for application areas including AI security
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dark-field STEM imaging, energy dispersive X-ray spectroscopy (EDS) and electron energy loss spectroscopy, at the intersection of electron microscopy, software engineering and machine learning. Major
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challenges. Our research and development capabilities include radar and optics technologies, radio frequency (RF) communications, computational imaging, artificial intelligence / machine learning (AI/ML
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challenges. Our research capabilities include radar and optics technologies, radio frequency (RF) communications, computational imaging, artificial intelligence / machine learning (AI/ML), neuromorphic sensing
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., NumPy, Pandas, SciPy, scikit-learn, PyTorch, TensorFlow) Experience developing and deploying machine learning or deep learning models Experience building and maintaining data processing pipelines
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., NumPy, Pandas, SciPy, scikit-learn, PyTorch, TensorFlow) Experience developing and deploying machine learning or deep learning models Experience building and maintaining data processing pipelines
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upgrade existing imaging and sensing systems Develop new sensing techniques and technologies Calibrate sensors and ensure data integrity for use in machine learning algorithms Support R&D projects
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strengths in any of these areas — quantitative imaging, modeling/transport science, machine learning, or scientific programming — are encouraged to apply. Major Duties/Responsibilities: Lead energy‑storage
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development teams Basic Qualifications: A Bachelor's degree in Computer Science, Electrical or Computer Engineering, or other field relevant to the job duties with 2-4 years of relevant experience. Competency
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experimental mechanics, mechanical behavior of materials, failure analysis Demonstrated experience in application of AI and machining learning in manufacturing processes Demonstrated experience in experiment