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Current Employees: If you are a current Staff, Faculty or Temporary employee at the University of Miami, please click here to log in to Workday to use the internal application process. To learn how
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Description Our Commitment Texas A&M University is committed to enriching the learning and working environment by promoting a culture that respects all perspectives, talents & lived experiences. Embracing
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-Phenomenology (hep-ph) , High Energy Physics , High Energy Theory , Machine Learning , Neutrino physics , Particle , Particle Physics , Particle Theory , QCD , Theoretical high energy physics , Theoretical High
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Current Employees: If you are a current Staff, Faculty or Temporary employee at the University of Miami, please click here to log in to Workday to use the internal application process. To learn how
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Research Associate will contribute to developing and/or applying artificial intelligence, machine learning, and image segmentation and/or data science-based methods to address cutting-edge topics, including
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of Engineering Science. The post is funded by EPSRC and is fixed term to the 31st January 2027. A2I explores core challenges in AI and machine learning to enable robots to robustly and effectively operate in complex, real
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. Responsibilities: • Design and conduct experimental research on paper-based materials for food packaging applications. • Develop predictive models for packaging performance using machine learning and physicochemical
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with rich clinical data. Advanced modeling of arrhythmias using generalized linear models and machine learning. Helps in training and mentoring of lab personnel, including graduate and undergraduate
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to joint research activities, publications, and surveys. Requirements PhD degree (or near completion) in robotics, control, machine learning, or a related field; Strong publication record demonstrating
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or recent Ph.D. in applied/computational mathematics, computational science, chemical engineering, or related disciplines. Ideal candidates will have strong past experience in scientific machine learning