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data Experience with GIS/RS and database environments (e.g., ArcGIS and Quantum GIS) Experience with machine learning and statistical learning Experience working with large, diverse datasets Familiarity
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Machine Learning, Statistics, Computer Science or closely related discipline. They will demonstrate an ability to publish, including the ability to produce high-quality academic writing. They will have the
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specialist knowledge in a relevant subject area. With knowledge of statistics and ability to use statistical packages for analysing data, you will have excellent communication skills and the ability to work co
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specialist knowledge in a relevant subject area. With knowledge of statistics and ability to use statistical packages for analysing data, you will have excellent communication skills and the ability to work co
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Special Instructions to Applicant Notice of Availability of the Annual Security and Fire Safety Report In compliance with the Jeanne Clery Disclosure of Campus Security Policy and Campus Crime Statistics
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Stanford University / SLAC National Accelerator Laboratory | Menlo Park, California | United States | 2 days ago
or superconducting sensors a plus) Vacuum / noble liquid systems Dilution refrigerators and superconducting detectors Detector simulation using Geant4 and/or G4CMP Statistical data analysis Collaborative experimental
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In compliance with the Jeanne Clery Disclosure of Campus Security Policy and Campus Crime Statistics Act (Clery Act), each year the University of Arizona releases an Annual Security Report (ASR
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the risks. You will have: a PhD in one of the relevant STEM disciplines, such as mathematics, statistics, computer sciences, theoretical food, ecological or physical sciences, etc. skills in mathematical
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. Expertise in statistics and data management Strong communication and interpersonal skills Ideal candidates have Experience in fMRI research Strong computer skills Promising publication record The anticipated
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studies. Develop and apply advanced statistical methods and machine learning techniques using tools such as R and Python. Integrate and run process-based models (e.g., crop models, hydrologic models