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We are offering a postdoc position on the development of statistical and tensor decomposition methods for representation learning of heterogeneous data with application to neuroimaging.Location
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Ecole Nationale Supérieure des Mines de Saint Etienne | Saint Etienne, Rhone Alpes | France | 17 days ago
statistical learning solutions applied to anomaly detection and adapted to the types of data coming from the manufacturing process. The aim is to optimize quality control by detecting potential anomalies among
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neurodegenerative diseases such as Alzheimer's disease using multimodal data from large databases. Statistical learning methods applied to the data will help identify multimodal phenotypes of the considered
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12 Feb 2024 Job Information Organisation/Company Université de Lorraine Department Laboratoire de Physique et Chimie Théoriques Research Field Physics » Statistical physics Chemistry » Physical
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respect people and their environment. We are looking for a post-doc to join our Image, Data, Signal department. We are looking for someone with a passion for computational statistics and machine learning
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through the EU Research Framework Programme? Not funded by an EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Researcher in statistical modelling and
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eliminate target species from the community. The prospective postdoc will address these topics through developing theoretical models that combine techniques from ecology, evolution and statistical physics
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that are expressed in a neuroinflammatory pathological context during protein aggregation processes. Advise experimenters on the best design for the statistical exploitation of data. Write the documents necessary
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the development of Danionella fish. Activity (and possibly behavioral) recordings will be combined with computational approaches combining statistical physics and machine learning to track the evolution
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an EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Quantitatively study the fractional statistics of anyons for different topological orders in