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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | 5 days ago
simplified setting Development of a prototype code (in Python, for example) for the reduced 1D model Writing of a final internship report Additional activities: Participation in the weekly ANEDP seminar
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setting. Creates database-to-deployment pipelines for models using the vital programming languages (primarily R, Python, SQL). Creates sustainable data science infrastructure and adheres to data analysis
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experience. Preferred Qualifications Experience working with programming language skills SQL, Python, R, Java script, Experience in visualization program such as Qlik Sense and Tableau experience is a plus
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University of California, San Francisco | San Francisco, California | United States | about 17 hours ago
implementing and maintaining AI/ML pipelines. Proficiency in MLOps, Python, SQL, and CI/CD is required. This role also requires a deep understanding of Epic data models (Clarity and Caboodle). Successful
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computational tools and models (Python-based) for protein sequence and structure prediction, design, and optimisation. Integrate computational predictions with experimental validation to create data-driven
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to: Design, prototype, and test laser-based systems for field use. Conduct field deployments including sensor installation, calibration, and troubleshooting. Write and implement Python-based scripts for sensor
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depend on the research group a successful candidate is associated with. Drs. Dickson, Feig, and Vermaas will expect that the researcher has prior experience with a programming language such as python and
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-analytic tools (e.g., Google Earth Engine, Python, R, QGIS). Support the application of machine learning and statistical methods to research projects. Develop documentation, training materials, workshops and
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. Outputs include a proof-of-concept scenario generator (Python toolkit), evaluation metrics suite, and academic papers in top finance and information systems venues. We welcome applicants with backgrounds in
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, financial technology, policy analysis, or academia. Ideal candidate: Background in computer science, data science, finance, economics, or related quantitative fields. Strong programming skills (Python/R