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Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung | Bremerhaven, Bremen | Germany | about 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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and/or Drishti software packages for reconstructing CT data. Basic knowledge about machine learning is of advantage. Very good English skills. High motivation to strive for scientific excellence and to
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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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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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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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20 Feb 2026 Job Information Organisation/Company Polish Air Force University (PAFU) Research Field Other Researcher Profile Established Researcher (R3) Positions Postdoc Positions Application
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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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-generated scenarios or machine learning-driven attack/defense strategies; Experience in developing comprehensive security assessments, producing technical reports, and contributing to toolkit documentation
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and/or cutting edges machine learning techniques to make foundational discoveries in reproductive medicine. The annual salary for this full-time position starts at $76,383, dependent upon skills 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