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develop and apply computational approaches for mass spectrometry data, with artificial intelligence/machine learning (AI/ML) being a major focus. They will have an opportunity to lead and contribute to a
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will use our Campus Values to guide their decisions and actions and demonstrate our Rebel spirit. PREFERRED QUALIFICATIONS Experience applying machine learning (ML) or artificial intelligence (AI
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in the Department of Urology at the Stanford University School of Medicine and will be affiliated with Department of Radiation Oncology and the Center for Artificial Intelligence in Medicine & Imaging
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for autonomous systems; field robotics; ● Cognitive Robotics: explainable artificial intelligence, perception-based interaction, and meta-reasoning to improve team performance; ●Human-Robot Interaction: Human
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professional deliverables ● Experience with causal inference, machine learning, and artificial intelligence is desirable ● Experience with clinical, EHR, or biobank data analyses is desirable