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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 1 month ago
and Experience: Methods and techniques of observational astronomy. Computer programming in languages such as Python. Oral and written communication skills, including scientific and technical writing
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 1 month ago
, geology, physics, or a related field; Strong written and oral communication skills. Desired: Prior experience in planetary science research; Quantitative programming skills; Experience with Geographic
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assays. Experience in catalysis, programming and large data analysis is a plus. Excellent communication skills and fluency in English (written and oral). Our offer The project is planned for 3 years
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Research Assistant (m/f/d) with a Ph.D. in Civil Engineering, Engineering Physics, Physics, Mathemat
. Strong scientific programming skills, preferably in Python, and experience with modern software-development practices Experience in implementing machine-learning and deep-learning methods Excellent
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analysis and programming (e.g. Python or MATLAB) is an asset. A self-motivated and organized working style, with the ability to work independently while contributing effectively to an interdisciplinary and
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data analyses in large cohorts, including large-scale mobile data collection. The fellow will work with our highly interdisciplinary research group, including scientists with expertise in
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quantitative and programming skills (preferably R), including data cleaning and statistical analysis; familiarity with diagnostic/screening test evaluation methods (e.g., sensitivity, specificity, ROC
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populations that are dynamic, scenario-conditioned and suitable for stress-testing mobility systems under disruptions. The work combines mobile phone data, census and urban data, activity-based and agent
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advantageous. Programming and data analysis skills in Python, MATLAB, R, or similar are advantageous. Proficiency in English; German is a plus. Enthusiasm for interdisciplinary research and
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as drone fleets, mobile robots and sensor networks increasingly use federated learning (FL) to train shared models across many devices without centralising raw data. In realistic deployments, however