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background in machine learning and deep learning, including experience developing, training and evaluating models, preferably for image analysis tasks. You have strong programming skills in Python
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, representation learning, mechanistic interpretability, concept-based explanation, or visual analytics for machine learning. Experience developing and applying deep learning methods at medium to large scale
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intersection of computer vision, deep learning, geospatial analysis and remote sensing, the postholder will collaborate closely with academic and stakeholder partners. The project addresses automated tree
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unique organ collections, and analyzed them using state-of-the-art bioinformatics approaches, increasingly incorporating AI and deep learning (see, e.g., Sarropoulos et al., Science 2026). This work
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[Malaysia Campus- VPSSP] An AI-Informed Planetary Health Framework for Equitable AMR Risk Mitigation
intervention.Insights from these engagements will be integrated with outputs from advanced AI models (e.g.,geospatial deep learning, graph neural networks, and explainable AI ) to refine the AMR risk maps and
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’ learning opportunities. For more information and how to apply: https://www.jobbnorge.no/en/available-jobs/job/308911/postdoctoral-fellow Where to apply Website https://www.jobbnorge.no/en/available-jobs/job
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following: life cycle assessment, life-cycle cost analysis, pavement simulation, machine learning, deep reinforcement learning, and/or physics-informed modeling frameworks; and demonstrated ability to
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Helmholtz Association of German Research Centres | Geesthacht, Schleswig-Holstein | Germany | about 2 months ago
data. Manage preferences for further information and to change your choices. Accept all cookies Reject optional cookies Skip to main content Postdoctoral Researcher - Probabilistic Deep Learning for
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and ability to work as team leader • Excellent written and oral communication skills Preferred Qualifications • Background in antisemitism studies • Experience with R, NLP and deep learning libraries
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d’Aidge (framework de deep learning développé au LIAE), leurs évaluations et leurs validations sur cible. Quelques références: - Cross-Architecture KD: https://openaccess.thecvf.com/content/ACCV2022