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Institut Supérieur de l'Aéronautique et de l'Espace | Toulouse, Midi Pyrenees | France | 28 days ago
mathematical and signal processing background, with strong skills in Matlab/Python. Knowledge on: GNSS; estimation theory; statistical analysis; are a plus. Demonstrated interest in remote sensing, GNSS, signal
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or Statistics or Business Intelligence, or Artificial Learning and Intelligence, or Signal Processing. o Skills in software development and databases. o Good general scientific background, particularly in
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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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, statistics, physics, biostatistics, bioinformatics or computer science.Type of contract: Temporary.Job status: Full-time.Duration: 36 months.Application deadline: 15/05/2024 23:59 - Europe/Brussels.Envisaged
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in molecular simulation is desirable, as well as a good knowledge of associated theoretical tools (statistical physics, etc.; programming experience including: Python, Fortran, C, C++, etc.). A good
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or materials science and/or statistical physics is required. Modalities: The thesis will begin on October 1, 2024. Interested candidates should send a letter of motivation, a CV, and at least one letter of
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statistical evaluation will be appreciated. The candidate should be curious, highly motivated, and flexible to address a highly inter-disciplinary project. Good English skills, writing and speaking, are needed
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, or natural language processing - Programming skills (e.g., Python, Matlab, JavaScript) - Knowledge of statistical analyses (R) Desired assets: - Experience with EEG - Experience with online testing
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of solutions, valorization and publication of results.This computer science thesis is part of a multidisciplinary environment involving researchers in demography, geography, statistics and computer
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Mines Paris - PSL, Centre PERSEE | Sophia Antipolis, Provence Alpes Cote d Azur | France | 25 days ago
integrate predictive models that reduce the complexity associated to multiple uncertainties, employ machine learning and/or statistical methods for high dimensional problems (e.g., sparse models, functional