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have a PhD in Civil Engineering, Engineering Mechanics, or Mechanical Engineering. Applicants are expected to demonstrate research experience in the fields of structural modeling and machine-learning
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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | about 4 hours ago
and graduate students Minimum Acceptable Education & Experience: PhD degree in Civil Engineering, Geography, or related fields Required Knowledge, Skills & Abilities: Proficient in geospatial data
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, structural sensing and health monitoring, conducting physical experiments, and validation of computational models. Required Qualifications: A successful applicant must have a PhD in Civil Engineering
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, structural sensing and health monitoring, conducting physical experiments, and validation of computational models. Required Qualifications: A successful applicant must have a PhD in Civil Engineering
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candidate must have as well a multidisciplinary scientific profile: PhD degree in materials science, chemical engineering, chemical Processes or another suitable engineering discipline. Knowledge about
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research with real-world impact. Qualifications and Skills are: PhD degree in Chemistry, Materials Sciences or a related engineering field. Proven experience in nano/micro-material synthesis. Good knowledge
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focus on charge injection, ion transfer, and structural dynamics in realistic and model systems for battery materials. The position will span experimental efforts at large scale X-ray facilities, handling
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learning. The post-holder will be familiar with the use of these techniques and experience of dataset construction and data mining will be essential. The successful applicant will have completed an MPhil/PhD
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. The candidate must hold a PhD in Urban or Rural Development, Civil Engineering or related domain. The candidate is expected to have hands-on experience in field related to urban or rural planning, renewable
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9738BR Kate Gleason College of Engineering KGCOE Mechanical Engineering Job Summary The Fluid-Acoustic-STructure (FAST) laboratory at Rochester Institute of Technology (RIT), directed by Dr. Byron D