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implement machine learning models dedicated to the prediction, interpretation, and quantitative analysis of Raman vibrational spectra, establishing explicit links between structure, local chemical environment
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machine learning methods as well as in biological data analysis are needed for the position. The postdoctoral researcher will play a leading role in this research, including methods development, data
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the project: Develop, train, and optimise deep learning models for wildlife species identification, classification, and segmentation using real-world datasets. Design and implement software modules to integrate
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Intelligence (AI), Machine Learning (ML), and data-driven decision support systems for precision agriculture. The Assistant Professor – AI & Data-Driven Precision Agriculture position is part of a strategic
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models for high-dimensional and functional data ”, led by Professor Valeria Vitelli. Successful candidates will work on Bayesian models for unsupervised learning when multiple data sources are available
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mechanics and analysis Experience with the following: Structural health monitoring (SHM) Finite element modeling (e.g., ABAQUS, SAP2000, ANSYS) Machine learning / AI (Python, TensorFlow, PyTorch) Demonstrated
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users, thanks to the use of machine learning tools and techno-economic analyses. This project is aligned with the sustainable development goals (SDG) 7 and 10 of the United Nations, by promoting a low
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of radiance data from new hyperspectral infrared instruments such as IASI-NG, MTG-IRS Enhancement of CrIS radiance assimilation algorithm are highly encouraged. - Use machine learning methods to cope with model
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associate in the broad areas of high performance computing and machine learning. HighZ is focused on developing scalable high order methods, enhanced with surrogate models for subscale physics, for modeling
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degree in AI, Computing Science, Mathematics, or Data Science. Strong coding, communication and organizational skills. Demonstrable experience with using machine learning packages (e.g., PyTorch