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, scalability, and effective performance across university use cases. Develops, trains, and fine-tunes machine learning models for a variety of university applications. Conducts experiments to evaluate model
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Sorbonne Université SIS (Sciences, Ingénierie, Santé) | Paris 15, le de France | France | about 8 hours ago
collaboration with L. Bonati at IIT Genova, who developed the library mlcolvar, https://github.com/luigibonati/mlcolvar ). 2) Compare the data-science dimensional reduction approaches above, with machine learning
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and machine learning. Knowledge of the basics of federated learning and causal inference is highly encouraged. Proven track record in research and development of machine learning algorithms. Proficiency
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Science, Computer Science, Data Science, Neuroscience, or a related field by the start date. Demonstrated expertise in computational modeling of human behavior or computer vision / machine learning
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, or SyCL/OpenCL. Hands-on experience with machine learning, including end-to-end training, tuning, and evaluation of at least one class of models. Working understanding of common machine learning model
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simulations. Two complementary strategies will be employed: structure-based virtual screening (docking simulations + molecular dynamics) and ligand-based virtual screening (machine learning models). We have
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strengths of the University of Tübingen in Computer Sciences and Machine Learning. Potential research directions include, but are not limited to, phylogenetic, demographic, ecological and biogeographic
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and optimization, we use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities
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/10.1016/j.xcrp.2022.101112 and https://doi.org/10.1080/08940886.2022.2114716 key words synchrotron radiation; X-ray Absorption Spectroscopy, machine learning, artificial analysis, autonomous experimentation
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programming models and high-performance computing techniques and machine learning models. Practical experience in the programming of high-performance computing of AI and/or scientific computing applications