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of Electrical Engineering , depending on the candidate’s profile and selected research track. About the research project DRIVE PR – Data-Driven Product Realisation is a large collaborative
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, numerical methods, or data-driven modeling of physical systems. Experience developing or applying computational models to physical or engineering systems. Proficiency in Python, C++, C, FORTRAN, or similar
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degree in Chemistry, Materials Science and Engineering, Chemical Engineering, or a closely related discipline. Strong hands-on experience in designing and conducting AI/ML-driven high-throughput
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. Our dedicated and compassionate faculty and staff are driven by a common mission: Contribute to innovative approaches in predicting, preventing, and curing diseases, shaping the future of medicine
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> on research projects related to kinetic equation and its related multiscale model reduction, numerical method, and data-driven/machine-learning approaches, including: • Numerical simulation of kinetic
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Position Summary The successful candidate will work with Dr. Kunlun Qi < https://kunlun-qi.github.io > on research projects related to kinetic equation and its related multiscale model reduction
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-driven mechanical characterization of solid and porous materials, supported by an NSF CMMI CDS&E award and industry-sponsored projects. The primary function of the position is to develop and cross
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About the Opportunity Job Summary The Postdoctoral Associate will conduct integrated experimental and computational research on informatics-driven mechanical characterization of solid and porous
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and resilience planning for the Treasure Valley. Unit URL https://iwrri.uidaho.edu/ Position Qualifications Required Experience Experience working with quantitative analysis, modeling, or data-driven
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: We invite applications for multiple Postdoctoral Fellow positions as part of the NSF AI Institute for Human-AI Cooperation (NSF HAIC) ( https://haicinstitute.org/), a $21.5 million multi