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to the development of deep learning methods to predict reaction outcomes and optimal reaction conditions for organic reactions. The work will involve model development using Python and/or other programming languages
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), Deep Neural Networks. Probabilistic Machine Learning and Time-series Analysis. Industrial applications of AI (energy, process industry, automation). Software development experience in teams. Programming
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University of California, Los Angeles | Los Angeles, California | United States | about 23 hours ago
of racial equity in schools, linkages between poverty, social inequality and education, education policy and the academic, social and emotional factors that impact student learning. • Exhibit a deep
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informatics, biomedical engineering, statistics, or related fields. The lab is engaged in developing novel deep learning and AI-based technologies for digital biopsies from medical images and real-world
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impact, leveraging one of the highest-quality financial datasets in the industry. What You’ll Do Conduct research and develop ML models to enhance trading strategies, with a focus on deep learning and
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, multisignal patches and wireless devices. Design and development of algorithms for multimodal biomedical signals based on Personalized Models, Deep Learning and Explainable AI. Applications to respiratory
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electronic health record (EHR) data; apply ML methods (especially deep learning methods) to solve critical medical problems. Implement methods into software that meets research needs, manage and update source
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- and electronics- workshops, but also with the NanoLab Amsterdam cleanroom facility situated in the neighboring NWO-institute AMOLF; develop deep-rooted expertise with and maintenance of WZI’s research
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cleanroom facility situated in the neighboring NWO-institute AMOLF; develop deep-rooted expertise with and maintenance of WZI’s research facilities and software packages; be able to act as advisor and expert
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Grant, focusing on the development of novel deep learning tools to recommend reaction conditions for the synthesis of novel TRPA1 inhibitors. The project “A machine learning approach to computer assisted