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or equivalent Skills/Qualifications - PhD in bioinformatics or related subjects - Expertise in python coding - Experience and good understanding of neural networks and machine learning - Fluent written and spoken
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PhD on top level in the field of architectural history and theory You bring founded experience in academic reserch and writing You bring appoved teaching experience from one or more academic
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At the Technical Faculty of IT and Design of the Department of Sustainability and Planning, Copenhagen, a position as Postdoctoral researcher in Geospatial Machine Learning for Predicting Land Use
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fellow devotes most of their time to research. There is the possibility of teaching up to 20%. Requirements Requirements PhD degree in in machine learning, automatic control, system identification, signal
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part-time post (0.2FTE) ideal for someone working in industry or with industry experience. This is because we want to bring in expertise with data processing and machine learning pipelines, and their
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-relationships, materials optimization, materials under extreme conditions, and generative AI. Candidates must possess substantial experience in artificial intelligence and machine learning methods, specifically
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track record in wireless communication systems, machine learning, or related fields. Ability to work independently and co-supervise PhD students. Excellent command of the English language, both written
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | about 1 month ago
disseminate the developed methods. Where to apply Website https://jobs.inria.fr/public/classic/en/offres/2025-09574 Requirements Skills/Qualifications PhD in Computer Science, Machine Learning, Bioinformatics
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difficult to couple with basin simulators. Geochemical metamodels, particularly those based on machine learning, can significantly reduce computation times while maintaining physico-chemical consistency
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interest in social science applications, and with strong competence in statistics and machine learning. The successful candidate will develop predictive models using machine learning and work alongside other