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curious to deliver work that matters, your journey starts here! The Civil and Environmental Engineering Department at Carnegie Mellon offers a unique interdisciplinary program that enables you to develop
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two-year Small Business Technology Transfer project (STTR) funded by the National Science Foundation. The successful candidate is expected to support the development, simulation and testing of model
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applicant will have: A PhD in Materials Science, Applied Physics, Electrical Engineering, Chemistry or a closely related discipline. Strong background in modeling and simulation of thermal/energy systems
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collaboratively with faculty, postdoctoral researchers, students, and other members of a multidisciplinary research team. Required Qualifications: Applicants must hold a PhD in Geotechnical Engineering or a closely
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National Energy Technology Laboratory (NETL) | Pittsburgh, Pennsylvania | United States | about 9 hours ago
projects with the Research Innovation Center (RIC) at the National Energy Technology Laboratory (NETL) in the area of Subsurface Simulation under the mentorship of Greg Lackey.This project will be hosted
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sciences Biological sciences » Biological engineering Researcher Profile Recognised Researcher (R2) Positions PhD Positions Application Deadline 15 Sep 2026 - 23:59 (Europe/Lisbon) Country Portugal Type
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Postdoc (f/m/d) Methods Development for the Micromechanical Characterization and Modeling of the ...
# Completed PhD in Materials Science, Physics, Materials Engineering, Nuclear Engineering, or a related field # Strong background in the microstructure and mechanical properties of metallic materials and their
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journals and conferences. Required Qualifications • Ph.D. in Electrical and Computer Engineering, Computer Science, or a related field. • PhD must be awarded no more than four years prior to the effective
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. Basic Qualifications: PhD in Chemistry, Materials Science & Engineering, Chemical Engineering, Polymer Science and Engineering, or a related discipline completed within the last five years and have a
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PhD in Physics, Applied Science, or a related discipline Experience in statistical physics, stochastic processes and/or data analysis methods Strong interest in engaging and collaborating with