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The continuous release of Earth Observation (satellite) data and the emergence of Machine Learning methods open up new possibilities for understanding forests. These large datasets provide
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algorithms. Our research integrates expertise from machine learning, optimization, control theory, and applied mathematics, spanning diverse application domains such as medicine, energy systems, biomedical
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PhD Scholarship Opportunity - Processing intelligence for green metals using in situ X-ray characterisation and machine learning Job No.: 693787 Location: Clayton campus Employment Type: Full-time
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About the project: Machine learning accelerated Inverse Design of Graphene Nanoribbons for Green Energy Supervisor: Dr Sara Sangtarash, University of Warwick Thermoelectric materials convert heat
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filled The overarching aim of this project is to find synergies between methods and ideas of modern machine learning and of statistical mechanics for the study of stochastic dynamics with application
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Applicants are invited for a PhD fellowship/scholarship at Graduate School of Technical Sciences, Aarhus University, Denmark, within the Electrical and Computer Engineering programme. The position
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | about 1 hour ago
Zhang, Jonathan Ashdown, Valeria Loscri, Francesco Restuccia, Finding a needle in a (Spectrum) haystack: Multi-band multi-device radio fingerprinting, Computer Networks, Volume 280, 2026, 112148, ISSN
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25 Sep 2026 Job Information Organisation/Company Universitat Politècnica de Catalunya (UPC)- BarcelonaTECH Research Field Engineering » Computer engineering Engineering » Communication engineering
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23 Sep 2026 Job Information Organisation/Company KU LEUVEN Department faculty of engineering science Research Field Computer science » Computer systems Computer science » Database management
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characterizing individual nanoclusters • Engineering and purify protein nanopores with tailored sensitivity to size, charge, and etc. • Developing data analysis pipelines and machine learning approaches for signal