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communication skills. Must be computer literate with proficiency and working knowledge of database and reporting tools such as Microsoft Word, Excel, Access, and PowerPoint. Preferred Qualifications A background
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, using technology, software, databases, and online tools to perform work. Excellent technological and analytical skills, including an advanced knowledge of Excel, Google Applications, Tableau, database
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, applications, tools, and services for the broader research community to use data at scale to pursue scientific inquiry and accelerate discovery. Learn more at https://gdc.cancer.gov/, https://gen3.org/, https
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using a variety of tools. Education and Experience: Bachelors degree required; masters degree preferred in data science or closely related field. Work experience will be considered in cases where degree
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of workplace safety including PPE use and reaction to emergencies and accidents. Ability to properly use and maintain hand tools associated with the trade including pipe vises and stands, pipe wrenches, levels
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Dalhousie University | Halifax Mid Harbour Nova Scotia Provincial Government, Nova Scotia | Canada | about 12 hours ago
equipment inventory. Support the surveying course, including in the use of GIS software and field data collection tools. Assist with data collection, processing, organization, and management from both
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and optimization, we use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities
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analysis tools (e.g., NetworkX or equivalent) Familiarity with permutation-based statistical methods (MR-QAP preferred) Interest in human-computer interaction Special Instructions to Applicants Budget
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) and machine-readable. Duties & Responsibilities Implement data best practices across the Center: Consult with research groups to enforce standard operating procedures for data collection, formatting
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Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial | Portugal | 27 days ago
simulations with data-driven machine learning approaches. By incorporating PINNs and vPINNs, the aim is to create robust numerical tools capable of simulating and predicting structural phenomena under different