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, air quality, and health. We seek a PhD or Postdoc to work on either of the two topics: Autonomous reaction discovery: combining quantum chemistry, machine learning, and automation to chart oxidation
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candidates will have experience in artificial intelligence, deep learning or decision support applied to RF sensing, wireless communications and signal processing. A strong background in multimodal data
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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | about 7 hours ago
validation, uncertainty assessment, data assimilation, model initialization, downscaling, or related model-data integration approaches. Excellent analytical, critical-thinking, and problem-solving skills
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Scholar must be an expert communicator, highly organized, and possess strong writing, analytic, and grantsmanship skills. The successful candidate will work with clinically annotated tumor cohorts, clinical
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learning or decision support applied to RF sensing, wireless communications and signal processing. A strong background in multimodal data analysis is expected, complemented by strong capacity for teamwork
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colleagues support each other and share expertise to enable everyone to benefit from a broader skills and knowledge base. Personal skills: Highly motivated, independent, and well-organized; strong analytical
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county across the state, Texas A&M AgriLife is uniquely positioned to improve lives, environments and the Texas economy through education, research, extension and service. Click here to learn more about
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with deep learning to design van der Waals heterostructures with optimised spin-orbit torque (SOT) efficiency for ultra-low-power memory and computing. Its two pillars are AUTOMATA, an automatic material
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Generation, digital double, human-like avatars. Profile PhD in Computer Vision Background in CS Research experience in gen AI, deep learning Strong computational and analytical skills, as well as experience
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studies how the infant brain learns about the social world and how early-life experience shapes neural circuits with long-term consequences for learning, attachment, and mental health. Our work focuses on