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the scientific discovery process by quickly extracting useful information from vast quantiles of data, and by providing feedback to the experiment system enabling autonomous control and steering. New fast data
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-aware multi-modal deep learning (DL) methods. At Argonne, we are developing physics-aware DL models for scientific data analysis, autonomous experiments and instrument tuning. By incorporating prior
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activities in vehicle automation and connectivity. Research existing industry leading autonomous perception and automation hardware and CAVs test methodologies with particular emphasis on collecting datasets
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the scientific discovery process by quickly extracting useful information from vast quantiles of data, and by providing feedback to the experiment system enabling autonomous control and steering. New fast data
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systems. The goals of such simulations is to analyze the potential impacts of future mobility technologies such as shared autonomous vehicles operating in fleets, vehicle electrification, new freight
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with experimentalists and experts in peptide self-assembly and lab automation, as we work towards advancing our autonomous discovery platforms. Preferred if candidate has experience in AI/ML and