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. Implement and optimize data representations and pipelines suitable for machine learning and uncertainty quantification. Collaborate with AI/ML experts to design and test inference methods that map
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research analysis on geothermal well development and other advanced energy technologies that could achieve transformative gains in energy efficiency. Ability to develop optimization and life cycle models
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understanding of machine learning methodologies and data science methodologies and techniques, (e.g. supervised and unsupervised learning, feature selection/engineering, hyperparameter optimization
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species. It can be fine-tuned for downstream applications such as predicting genetic perturbations, optimizing photosynthetic apparatus for performance, selecting top performing genotypes for various
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including code design, documentation and testing. Familiarity with optimization methods including Machine Learning (ML) techniques. Any experience with computations on GPUs. Working knowledge of Linux command
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engineering or equivalent field and 0-2 years of research experience. Background in quantitative analysis, mathematical modeling, data science and simulation techniques with expertise in optimization techniques