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on Artificial Intelligence Organization, 2017. - C. Bouveyron, P. Latouche and R. Zreik, The Stochastic Topic Block Model for the Clustering of Networks with Textual Edges, Statistics and Computing, vol. 28(1
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In cancer as well as chronic infections, T cells are exposed to persistent antigens and acquire a dysfunctional gene expression program which includes high expression of the inhibitory receptor
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or equivalent in data science, signal processing or applied mathematics and will require a strong background in theoretical as well as computational aspects of linear algebra, optimization and signal processing
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About us The University of Luxembourg is an international research university with a distinctly multilingual and interdisciplinary character. The Faculty of Science, Technology, and Medicine
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(FSTM) at the University of Luxembourg contributes multidisciplinary expertise in the fields of Mathematics, Physics, Engineering, Computer Science, Life Sciences and Medicine. Through its dual mission
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Apply by sending an email directly to the supervisor. The application will include: • Letter of recommendation of the supervisor indicated above • Curriculum vitæ • Motivation letter • Academic transcripts of a master’s degree(s) or equivalent • At least, one letter of recommendation •...
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anticipate risks by integrating all human, technical, and organizational factors into a dynamic model of the OR. This project will be developed within the ICARE team (Artificial Intelligence, Computer Science
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Master's degree in Epidemiology, Public Health or a related field. Applicants should be enrolled in a Master program and the internship should be a mandatory part of the diploma; Experience in working
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master’s degree in mathematics, physics or informatics with a strong knowledge in machine learning. Skills: Coding in Python and/or R is required. Previous knowledge in archaeology and zoo-archaeology would
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, copyrighted, or biased. By studying brain data recordings and building computational models that mimic real populations of neurons, the project aims to uncover active unlearning: how the brain learns