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background in machine learning, predictive modeling, or applied AI Proficiency in Python and/or R; experience with libraries like scikit-learn, XGBoost, TensorFlow. -Experience working with real-world datasets
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integration. Lead and contribute to research involving AI-powered and AI-enabled robotic systems, including deep reinforcement learning, computer vision, and human-robot interaction. Facilitate strategic
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to implement advanced computational pipelines, including machine learning, deep learning, Bayesian inference, and probabilistic mixed membership modeling for innovative research. · Contribute
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. As a Carnegie Doctoral/R2 institution, our world-class scholars instruct about 26,000 students in associate's, bachelor's, master's and doctoral level degree programs. Whether you are seeking the charm
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supervise graduate students. Mentor graduate students in modeling and data analysis using machine learning. Guide students in developing coding skills using the latest AI models. Host regular individual and
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implement computational models and perform deep learning analyses for the early detection of Alzheimer’s disease using MRI, biomarkers, and advanced computational techniques · Collaborate with a
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) Corrosion behavior (electrochemistry & high-temperature oxidation) In-situ monitoring of AM processes Computational skills in: Phase-field modeling, Machine Learning, FEM, DEM, COMSOL Alloy design (CALPHAD
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groups led primarily by Professor Tarek Abdoun and Professor Mostafa Mobasher. It involves a diverse range of activities including: structural and geotechnical modeling, machine-learning model development
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policies pertaining to other schools at Duke University. The postdoc candidate is expected to: 1) Develop novel methods for incorporating scientific machine learning in solving problems in solid mechanics
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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