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interpretable autonomous experimentation systems remains a major research challenge. The successful candidate will develop reinforcement-learning and decision-making algorithms for autonomous laboratory platforms
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and conferences. Proven experience in design and implementation of deep learning algorithms. Outstanding programming skills in Python. Extensive experience working on one or more of the following areas
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. Responsibilities include (but not limited to): Lead the development of the NC-ARPES technique (hardware, post-processing algorithm, theory, data interpretation) Propose and perform new TR-ARPES studies of quantum
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