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physics. Development of new artificial-intelligence and machine-learning techniques for high-energy and nuclear physics. Close interaction with our collaborators in the EIC and the BNL theory group will be
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machine learning, computer vision, human-computer interaction, or similar relevant areas. Experience in research or development on bias, interpretability, and/or privacy in machine learning/AI is necessary
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(e.g., based on physiological signals or direct inputs from occupants) and developing algorithms, including machine learning methods. The work will include statistical modelling, data-driven modelling
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engineering, or a related field. · Strong background in machine learning or data analytics and hands-on experience handling big data. · At least one year of research experience in transportation
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, machine learning, and control in the energy sector. The postdoc researcher will perform theoretical study and algorithm development on optimization/control/data analytics methods and authorize peer-reviewed
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Qualifications: PhD in experimental particle physics at the time of appointment. Preferred: Deep understanding of the particle detectors, particle identification, data analysis Machine learning experience is a
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. The primary objective is to design robust and efficient planning solutions—integrated within a digital twin—that account for the uncertainties and variability inherent in industrial processes. Machine learning
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-based or machine-learning/AI-based climate modeling (e.g. hydrometeorological and/or atmospheric processes) are particularly encouraged to apply. Position 3 Working with Dr. Kelly Baker , EEH Associate
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(e.g., finite element or wave propagation simulations) for defect detection and materials analysis Integrate AI, machine learning, and robotics into NDE and manufacturing processes for automation and
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Standing or sitting up to 8 hours/day, lifting <30 lbs, may view computer up to 8 hours/day. Shift Some flexibility, generally Monday- Friday, 8-5 Job Summary This position is designed to have 60-70 percent