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Postdoctoral Research Associate- AI/ML Accelerated Theory Modeling & Simulation for Microelectronics
, Northwestern University, and Lawrence Berkeley National Laboratory to address grand challenge problems in materials for next-generation microelectronics applications. The position resides in the Theory
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of refractory and other high temperature alloys. Key activities include materials processing, microstructural characterization using scanning electron microscopy and electron backscatter diffraction, and
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, electronic structure theory (e.g., density functional theory), spectroscopic mapping/modeling, and AI/ML to investigate molecular reactivity in liquid media. The postdoc will work within a multidisciplinary
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milestones. Major Duties/Responsibilities: Utilize techniques such as optical microscopy, scanning electron microscopy (SEM), electron backscatter diffraction (EBSD), and transmission electron microscopy (TEM
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optimization of scientific applications, particularly molecular dynamics and machine learning applications. Familiarity with electronic structure and quantum chemistry methods, such as density functional theory
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photonic quantum sensing and computing. Experience with control electronics, data acquisition systems, machine learning and AI for control and optimization of experimental apparatus. Experience with vacuum
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from third party vendors will not be accepted; these resumes will be deleted and the candidates submitted will not be considered for employment. If you have trouble applying for a position, please email
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not be accepted; these resumes will be deleted and the candidates submitted will not be considered for employment. If you have trouble applying for a position, please email [email protected]. ORNL
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. Represent ORNL and the Learning Systems Group by giving talks at public forums (conferences, workshops, guest lectures, etc.). Basic Requirements: Ph.D. in Computer Engineering, Electrical Engineering
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for Computational Sciences. They will collaborate with leading computer and computational scientists at ORNL and external collaborators in the development and application of new computational techniques specifically