606 structural-engineering-"https:"-"https:"-"https:"-"https:"-"https:"-"FEMTO-ST" positions at Pennsylvania State University
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this mission by providing reliable, cutting-edge technology support to faculty, staff, and students. We are seeking an Information Technology Support Specialist who is passionate about problem-solving and
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from supervisor. Preferred Qualifications: A. bachelor’s degree or higher in a field such as Food Science, Agricultural Engineering or a related field; prior experience in food-processing, experience in
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, engineering, and technology problems in support of the Navy, the Department of Defense (DoD), and the Intel Community (IC). FOR FURTHER INFORMATION on ARL, visit our web site at www.arl.psu.edu . BACKGROUND
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comprehensive applications and engineered solutions for specific projects, as well as providing technical support to the field organization. This hybrid/office-based position is responsible for the technical
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State experience for all stakeholders. If you are passionate about using technology and data-driven insights to drive innovation, collaboration, and measurable impact in a complex and dynamic environment
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State. You will direct a team of engineers in designing test and evaluation procedures, developing prototypes, and implementing lab-based and outdoor testing of systems for multiple DoD and IC sponsors
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Research Laboratory (ARL) at Penn State. You will report directly to the Director of Spectrum and Signatures Solutions Division and be responsible for managing the division’s research engineers, scientists
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, including tracking, delivery coordination, and resolving account issues. Manage circulating technology by troubleshooting hardware/software issues, coordinating with IT teams, and maintaining equipment
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academic term. The location where the student will perform duties is 418 Earth and Engineering Sciences Building, University Park. Primary Duties: This position will be responsible for work on various
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Apply advanced data mining, statistical modeling, graph algorithms, and machine learning to extract insights from large structured and unstructured datasets Mentor junior data scientists and analysts