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large environmental and ecological data sets, incl. empirical, remote-sensing and simulated model data knowledge in programming languages (UNIX, C, or similar) and script-based programming (R, Python
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innovative machine learning architectures for the mining, prediction, and design of enzymes. Combine state-of-the-art ML (e.g., deep learning, generative models) with computational biochemistry tools
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