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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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collaborating with a software engineering team to translate research into production-ready tools. The successful candidate will be part of an inter-lab, highly inter-disciplinary team of experts in ML, applied
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. Experience with experimental physics and/or engineering is a plus. Presentation and documentation skills for describing research and clearly communicating results and data. Excellent collaboration skills and
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, distributed systems, or large-scale data pipelines Experience with game engines, XR systems, or advanced graphics frameworks applied to scientific data Record of publications in visualization, AI
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Requirements Recent or soon-to-be-completed PhD (typically completed within the last 0-5 years) in chemistry, chemical engineering or materials science (those with other degrees but have similar skills to those
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collaborative effort across XSD, the Materials Science Division (MSD), and the Chemical Sciences and Engineering Division (CSE). Key Responsibilities: Develop and enhance pump–probe sub-nanosecond TXS method