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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 1 day ago
methodology, machine learning, and biomedical data science. Our research develops rigorous and interpretable methods for high-dimensional biomedical data, with applications spanning cancer genomics
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science, or a related field; experience with using and building machine learning models, developing and validating computational analysis workflows, and developing circuit models is preferred; excellent
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-physical systems security, protection of critical infrastructure, and adversarial machine learning. The position will involve collaborating with faculty and graduate students on interdisciplinary research
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. The postdoctoral researcher will conduct cutting-edge research in areas such as cyber-physical systems security, protection of critical infrastructure, and adversarial machine learning. The position will involve
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, scikit-learn, PyTorch, TensorFlow); additional experience with R, MATLAB, or Julia is an advantage. Machine Learning Expertise: Familiarity with causal machine learning, ensemble methods, and deep learning
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, scikit-learn, PyTorch, TensorFlow); additional experience with R, MATLAB, or Julia is an advantage. Machine Learning Expertise: Familiarity with causal machine learning, ensemble methods, and deep learning
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correspondence between algorithmic computation of differentials or solutions to differential equations and the logical principles at work in linear logic and associated lambda calculi. The postdoctoral researcher
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, or similar, and a degree in Environmental Engineering, Environmental Science, or a related quantitative field. Position 2 will focus on large-scale data analytics and machine learning. Applicants should have
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enterprises (SMEs). The postdoc will work at the intersection of cybersecurity, machine learning, and human centered system design, contributing to the research on privacy aware monitoring, attacker modelling
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Union within the project “A Comprehensive Trustworthy Framework for Connected Machine Learning and Secure Interconnected AI Solutions (CoEvolution)”, - CUP F23C24000210006 – selection code: ipd_10D_0426_09/IINF