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. Mingjie Liu, focusing on the development and implementation in advanced machine learning and deep learning models to predict new materials and molecule properties. This one-year appointment is renewable
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for a highly motivated individual with a solid background in nutrition and research experience in bioinformatics, particularly in single-cell data integration and machine learning. You should have an in
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theory, graph transformation, algorithm engineering, machine learning. Proven competences in programming and ease with formal thinking are a necessity. The general goal of the TACsy project is to expand
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the areas of: Sustainable/resilient chemical supply chains Artificial Intelligence /Machine Learning for the prediction of bulk properties in performance molecules and materials Synthetic electrochemical
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data-driven. Will you be part of that change? Then join us in this unique program! Your goal will to leverage cutting-edge machine learning methodologies to extract conformational ensembles from single
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machine learning methodologies to extract conformational ensembles from single-particle cryo-EM data. The project builds on our recently established (not yet published) software, which machine-learns
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leading-edge data analysis, including machine learning/AI approaches. We are looking to attract applicants from chemistry/materials chemistry with an innate drive and curiosity to deliver new solutions
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use a multi-discipline approach incorporating organic and polymer chemistry, materials science and leading-edge data analysis, including machine learning/AI approaches. We are looking to attract
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learning methodologies to extract conformational ensembles from single-particle cryo-EM data. The project builds on our recently established (not yet published) software, which machine-learns protein
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. Applicants must have a minimum of 3 years of experience in Chemistry instruction at the post-secondary level with demonstrated implementation of Active Learning pedagogy. Experience working with near peer