29 algorithm-development-"Prof"-"Prof"-"Prof" Postdoctoral positions at Carnegie Mellon University
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members Participate in traffic scenario generation project and pedestrian modeling project. Develop sophisticated AI-driven algorithms that create realistic, safety-critical test scenarios for autonomous
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by working to develop novel algorithms on finite element method, isogeometric analysis, geometric modeling, machine learning and digital twins to study various applications such as computational
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algorithmic fairness Formulation of new problems and research directions and translating topical issues into algorithmic problems Designing new algorithms and investigating their performance on synthetic and
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algorithmic fairness Formulation of new problems and research directions and translating topical issues into algorithmic problems Designing new algorithms and investigating their performance on synthetic and
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, engineering, and development responsibilities as assigned by the supervisor Adaptability, excellence, and passion are vital qualities within Carnegie Mellon University. We are in search of a team member who can
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fundamental research and applied validation underlying a multi-chamber cancer-detection device from concept through integrated laboratory demonstration. You will develop user-oriented sample-handling and assay
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multidisciplinary team tackling critical issues in cancer detection and provide support in the applied nanotechnology lab to assist in the development and execution of research projects related to the production and
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multidisciplinary team tackling critical issues in cancer detection and provide support in the applied nanotechnology lab to assist in the development and execution of research projects related to the production and
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. This project provides a vibrant learning environment for all the trainees. The PI is committed to the professional development of the postdoc associate in addition to their technical excellence. Core
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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