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Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung | Bremerhaven, Bremen | Germany | 2 months ago
deep learning (x/f/d/m) Background With the project Deepcloud, we will leverage the machine-learning revolution to understand clouds and their role in the climate system. We aim to train a deep learning
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projects in data-driven nutrition, such as: statistical modelling, AI, and machine learning on large epidemiological cohorts, diet and health data analysis of omics data (metabolomics, proteomics, microbiome
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development of analytical solutions, data analysis and machine learning. Candidates should have a demonstrated record of scientific publications in international journals and participation in conferences
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and behavioural speech features. Integrate neuroimaging, speech and clinical data using multivariate and machine-learning approaches (e.g. UMAP). Investigate the effects of deep brain stimulation
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, embracing failure as a learning opportunity, and continuously enhancing our knowledge and methods to tackle local, national, and global challenges. The postdoctoral associate will work directly with both
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research teams that include faculty, graduate students, and undergraduate students. One student’s research used machine learning to solve physics problems, while another’s helped create a data science
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behavior of these components will be developed based on Finite Element Methods (FEM) complemented by Machine Learning models. Legislation and Regulations: Statute of Scientific Research Fellow, approved by
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Polytechnique de Paris. The group conducts research at the intersection of statistical learning, machine learning, and data science, with a strong focus on structured data, representation learning, and
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these autonomy and self-adaptation capabilities. Three major challenges have been identified: (P1) modelling uncertain environments where robust, weakly supervised machine learning algorithms can be deployed
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: genetics, epigenetics, inflammation, metabolic pathology, autoinflammatory pathology, autoimmunity, arthritis, computational analysis, mathematical modeling, applied algorithms, machine learning in biology