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Ulla Schildt/NHM 8th April 2026 Languages English English English Natural History Museum Postdoctoral Researcher in Spatial Ecological Modeling and Interdisciplinary Biodiversity Science Apply
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research background in at least one of the following areas: Ontologies, Knowledge Graphs, and Knowledge-based Systems, or Natural Language Processing, Large/Small Language Models, and/or Transformer-based
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to develop these further Fluency in English is required; good command of French, Luxembourgish and / or German is considered an asset We offer Multilingual and international character. Modern institution with
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● Preparing complex data for analyses ● Conducting advanced quantitative analyses ● Drafting, submitting, and revising (in English) academic articles for publication in leading international journals in
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than 1,800 employees and nearly 21,600 students. The Process Metallurgy research group at the Department of Civil, Environmental and Natural Resources Engineering is seeking a highly motivated postdoctoral
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prestigious ERC Consolidator Grant in a great interdisciplinary institute in Amsterdam. Join us! Are you passionate about machine learning, natural language processing and generative AI? We are seeking a
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 6 days ago
machine learning applied to natural language processing, computer vision, or a related area. Strong publication record in NLP, ML, or related areas -Strong programming skills, including TensorFlow and/or
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in natural and perturbed conditions”). This project, led by the University of Lille in close collaboration with Sorbonne University and international partners, addresses the critical challenge
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Are you passionate about pushing the frontier of catalysis science? The Center for Visualizing Catalytic Processes (VISION) at the Technical University of Denmark (DTU) is offering mutiple Postdoc
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evidence from diverse sources, analyze international survey data, and contribute to the development of a conceptual framework that captures the multi-level and intersectional nature of stigma. Findings from