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Postdoctoral Research Associate- AI/ML Accelerated Theory Modeling & Simulation for Microelectronics
that can incorporate multi-scale computational simulations to aid with data fusion across multiple modalities of experiments with the final goal of discovering novel materials phenomena or even new materials
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coding (Python) for building energy modeling and controls Preferred Qualifications: Expertise in modern optimal control techniques (e.g., AI based controls) High level of competence in coding and scripting
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languages, such as Python, Igor, and labview. Excellent written and oral communication skills. Motivated self-starter with the ability to work independently and to participate creatively in collaborative
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knowledge of models of strongly correlated electron systems. Proficiency with scripting or programmatic languages, such as Python, c, and MATLAB. Excellent written and oral communication skills. Motivated
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technologies Proficiency in using Excel, Python, R or similar tools for engineering data analysis is preferrable Excellent written and oral communication skills Motivated self-starter with the ability to work
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languages, such as Python and C++. Demonstrated experience in structural materials, such as research in phase transformations or mechanical behavior. Demonstrated effective written and oral
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. Basic Qualifications: A PhD degree in civil, chemical, or environmental engineering. A minimum of 2 years of experience in the use of Python for programming of data analytical models and algorithms
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. Demonstrated expertise in writing advanced software in Python. Demonstrated experience with the LINUX operating system, LaTeX, Git, Python. Demonstrated expertise in the design and implementation of deep
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develop cutting-edge differential privacy techniques for large-scale models across multiple institutions. This position offers a unique opportunity to work with the world's first exascale system
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experience in scientific data visualization, AI/ML, or a related field. Proficiency in the Python and C++ programming languages. Preferred Qualifications: Strong publication record with publications in