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satisfactory performance. Degree/Field of Study: PhD in fluid dynamics, geomechanics, computational geomechanics by start date with Penn State. Applications must be submitted electronically and include a CV
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tools (e.g., light sheet fluorescent microscopy) and computational approaches to examine cellular resolution details in the whole mouse brain. Moreover, we use systems neuroscience approaches (e.g
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engineering or a related engineering program, are encouraged to apply. Duties will include computer-based data collection and data entry. Applicants should be familiar with MS Excel. It is anticipated
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physics research under my supervision on topics including quantum many-body physics and quantum statistical mechanics. The research is expected to be partly analytical and partly computational in nature
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REQUIREMENTS The Mathematics Department in the Eberly College of Science has a vacancy available for a Part-Time Research Assistant/Associate. The selected candidate will work on computational modeling and
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. • Rich opportunities will be available for innovative equipment and process development, advanced materials characterization and deep collaborations with theoretical/computational efforts focused
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APPLICATION INSTRUCTIONS: CURRENT PENN STATE EMPLOYEE (faculty, staff, technical service, or student), please login to Workday to complete the internal application process . Please do not apply here, apply internally through Workday. CURRENT PENN STATE STUDENT (not employed previously at the...
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REQUIREMENTS: The Office of Technology Transfer (OTT) is inviting candidates with an interest in technology commercialization to apply for an opportunity to join a one-year paid fellowship program called
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computer-based analysis skills a background in physics strong communication skills Compensation: The starting rate for this job is $10/hour. BACKGROUND CHECKS/CLEARANCES Employment with the University will
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, Robotics, Computer Vision, or related disciplines. Proven expertise and hands-on experience in one or more of the following areas: large language models (LLMs), end-to-end learning, AV localization