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will conduct research to enhance their readiness for successful and impactful careers in national security. Typical duties may include: Support engineering testing, data analysis, and data visualization
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imagery, and environmental data layers used in research workflows Utilize ArcGIS Pro, ArcGIS Online, and related tools to develop maps, spatial visualizations, and analytical products for internal research
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, contributing to study design, data analysis, visualization, and interpretation while helping to build sustainable computational infrastructure within the department. Key Responsibilities Develop, select, and
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to promote effective and consistent data practices Required Qualifications: Proficiency in SQL, database management, and ETL (Extract, Transform, Load) processes Experience with data visualization tools
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visualization, and other emerging methods. This position will lead training and mentorship across interdisciplinary teams of students, faculty, and staff on data workflows across multiple projects and areas
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analysis, software support, and architectural documentation. This internship offers hands-on experience in managing and analyzing campus space data, supporting SaaS platforms, and working with architectural
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: Collaborate with NCEMS Working Groups to design and implement computational methods for integrating, analyzing, and visualizing complex molecular and cellular biology data Lead data wrangling, harmonization
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. Approval of remote and hybrid work is not guaranteed regardless of work location. For additional information on remote work at Penn State, see Notice to Out of State Applicants . JOB DESCRIPTION AND POSITION
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staff assignments and manage workflow to ensure deadlines are met across multiple simultaneous recruitment cycles. Data Analytics, ROI & Performance Measurement ROI Analysis: Establish a robust reporting
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Collaborate with NCEMS Working Groups to design and apply rigorous statistical approaches for integrating, analyzing, and visualizing complex molecular and cellular biology data. Apply inferential statistics