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The successful candidate will develop generative machine-learning methods for amorphous molecular thin films — the supramolecular structures that govern the performance of organic-electronic materials
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evaluate large language models (LLMs) assisted methods for extracting, organising and assessing complex scientific information related to materials. The initial application will be phase diagrams and related
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-understanding. In addition to purely academic doctorates, the university also enables and promotes doctoral projects with a strong artistic component, provided that the relationship between the artistic work and
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SINTEF Ocean; FME Martrans https://martrans.no/ and the Norwegian Maritime AI Centre https://www.ntnu.edu/mai The research is foreseen to focus on fluid-dynamic properties of the different sail types
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biogeochemical cycles 4. The impact of climate change and extreme events on the soil microbiome 1. Tabatabai, M.A. (1994). Soil Enzymes. In Methods of Soil Analysis (eds R.W. Weaver, S. Angle, P. Bottomley, D
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specific requirements: a) Experience in the application of data analysis and machine learning methods to scientific data (e.g. multivariate analysis, chemometrics, neural networks, classification
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | about 2 months ago
of CMatch in OAEI 2025. https://ceur-ws.org/Vol-4144/om2025-oaei-paper3.pdf [7] Sousa, G., Lima, R., & Trojahn, C. (2026). Survey on embedding methods applied to ontology matching. The expected work consists
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Posting Summary Logo Posting Number RTF00162PO26 USC Market Title Senior Research Associate Link to USC Market Title https://uscjobs.sc.edu/titles/156389 Business Title (Internal Title) MCH Lead
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sensors capable of characterizing the dynamic state of buildings and their inhabitants. By combining multi-microphone acquisition techniques with state-of-the-art machine learning methods, acoustic sensing
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this will be addressed using advanced NMR methods. and integrative modelling, providing real- insights into conformational changes governing CB1R selectivity. This project offers the opportunity