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English Proficiency in machine learning and large omics data analysis is preferred. Where to apply Website https://www.lih.lu/en/job/?value=JA/PDGMB0326/MD/DIIA Requirements Research FieldComputer science
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Description Within the ANR HEBBIAN contract, the objective is to adapt bio-inspired Hebbian learning models recently proposed by one of the partners of this ANR (Frédéric Lavigne) in order to account for data
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models for high-dimensional and functional data ”, led by Professor Valeria Vitelli. Successful candidates will work on Bayesian models for unsupervised learning when multiple data sources are available
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for the analysis of hyperspectral imaging data applied to pictorial layers, based on coupling physical radiative transfer models (two-flux and four-flux approaches) with machine learning methods. The researcher will
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advanced retrieval techniques, including spatio-temporal regularization, and hybrid methods with machine learning and deep learning. He/she will support the development of an improved forest RTM that can
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data analysis methods to study biological memory circuits and their applications to machine learning. Building on recent work from the Fiete Lab, the role focuses on identifying principles of biological
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. For more information on the work of the group please see https://www.maynoothuniversity.ie/physics/our-research. This position is part of the CARINA project which is funded through a Research Ireland
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of Barcelona; Particle Physics Phenomenology group. Main responsibilities / tasks: 1. Develop anomaly detection methods using Machine Learning and Simulation-Based Inference for high-dimensional parameter spaces
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responsibilities. Job Description: Dr. Feifei Xiao's laboratory (https://directory.ufhealth.org/xiao-feifei ) at the University of Florida's Department of Biostatistics (https://biostat.ufl.edu/ ) is accepting
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to apply Website https://emploi.cnrs.fr/Offres/CDD/UMR8197-VALHER-223/Default.aspx Requirements Research FieldMathematicsEducation LevelPhD or equivalent LanguagesFRENCHLevelBasic Research