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together computer scientists, AI researchers, domain scientists, software engineers, and high-performance computing experts. You will help design and implement new methods for multimodal federated learning
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for Microelectronics” —a physics-informed AI framework that links composition, structure, and operating conditions to defect evolution and functional performance. The successful candidates will lead experimental
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integrating literature, in-house, and newly generated experimental data Build surrogate and predictive models that connect composition, molecular structure, synthesis and processing conditions, morphology, and
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, Simulink, Stateflow and preferably Autonomie. Well-developed problem-solving, systems thinking, quantitative, and critical thinking skills; intellectual curiosity. Able to structure and formulate solutions
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ecosystem. Key Responsibilities Develop and optimize lithographic patterning of nano- and meso-scale structures, such as gratings, waveguides, cavities, and metamaterials for quantum and THz devices Integrate
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optimization schemes. From developing AI models to uncover structure-function relationships with limited data sets, to building automated electrode-electrolyte interface discovery workflows and implementing full
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harness the nonequilibrium correlation between structural, charge, and spin/pseudospin degrees of freedom in two-dimensional (2D) materials. The success of this program will lead to new means to control
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four staff members [Ian Cloët, Alessandro Lovato, Anna McCoy, and Yong Zhao] and several postdocs and students. The group has a broad research program in QCD/hadron physics and nuclear structure