39 machine-learning-"https:"-"https:"-"https:" Postdoctoral positions at Carnegie Mellon University
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Designing and extending algorithms grounded in probabilistic machine learning Applying statistical techniques to assess robustness and generalization. Development of methods of research, testing and data
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. Modeling dynamical systems Designing and extending algorithms grounded in probabilistic machine learning Applying statistical techniques to assess robustness and generalization. Development of methods
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. Modeling dynamical systems Designing and extending algorithms grounded in probabilistic machine learning Applying statistical techniques to assess robustness and generalization. Development of methods
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vehicle validation. Design and implement machine learning models that capture the complexity and variability of real-world traffic situations, including unusual pedestrian behaviors, edge cases, and multi
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vehicle validation. Design and implement machine learning models that capture the complexity and variability of real-world traffic situations, including unusual pedestrian behaviors, edge cases, and multi
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opportunity for someone who thrives in an interesting and challenging work environment. Core responsibilities include: Research in the intersection of discrete optimization and machine learning, including
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opportunity for someone who thrives in an interesting and challenging work environment. Core responsibilities include: Research in the intersection of discrete optimization and machine learning, including
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scientific machine learning. Interest in interdisciplinary efforts is also valued. Commitment to and experience in teaching and mentoring student research are also desirable. Application Instructions
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events. Maintain and perform analysis on large quantitative datasets; develop and implement statistical or machine learning models to recover patterns of technology adoption, task organization and skill
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events. Maintain and perform analysis on large quantitative datasets; develop and implement statistical or machine learning models to recover patterns of technology adoption, task organization and skill