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for renewal for an additional term based on institutional needs. Visiting Lecturers carry a 5-5 teaching load. We seek to hire a candidate with strong teaching credentials to teach our introductory course in
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developed countries, smartphone penetration exceeds 80%. The automatic transport mode detection (TMD), when effectively exploited, possibly using some kind of machine learning algorithm, provides more
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to machine learning algorithms in order to get uncertainty estimates for parameters governing the distribution of the observed data. The predictive Bayes scheme for uncertainty quantification contains a wide
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instructor to teach a total of six courses over the course of one year. In Summer 2026, the instructor will be responsible for teaching LA 7005, LA Design Studio NYC, and LA 7006, LA Design Seminar NYC, both
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, speaking); French is a plus but not mandatory. - Strong background in ecology. - Experience with statistical analysis using R; interest in machine learning is an asset. - Prior experience with one or more of
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landscape constrains or enables discovery. The project draws on tools from topological data analysis (e.g., persistent homology, Euler characteristic curves, discrete curvature), machine learning (e.g
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the application of these methods to problems in the physics of oxides, semiconductors, metals and their surfaces. Machine learning methods are used to close the complexity gap. Currently, the group consists
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to eligible team members. Learn more at https://hr.duke.edu/benefits/ Minimum Qualifications Education See job description for education requirements. Experience See job description for requirements. Degrees
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of computational methods that enable machines to perform tasks requiring perception, learning, reasoning, and decision-making. It encompasses core areas such as machine learning, data-driven modeling, intelligent
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Job description: DESY The CMS Quantum Computing group develops generative machine learning models for detector simulations, specifically the simulation of showers in calorimeters: Proof-of-principle