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.): Research within hydrometallurgy, redox–dissolution chemistry, battery recycling, and data-driven modelling Develop and apply mechanistic and data-driven models to identify reaction regimes and predict
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candidate will join the Scientific Machine Learning group at TDB and SciLifeLab. The group develops theory, methods and software for data-driven science, with a current focus on uncertainty quantification
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of engagement. Read more at: Department of Computing Science Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data-driven
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research environments in Computational Science, the research and education has a unique breadth, with large activities in areas such as numerical analysis, mathematical modelling, development and analysis
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geoinformatics and spatial data science, including demonstrated knowledge of GIS, spatial analysis, spatial modelling, and GeoAI. Experience and expertise in WebGIS development, familiar with the core technologies
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-bandgap-key-future-green-electronics, https://c3nit.se/ Subject description The purpose of this project is to develop a data-driven approach for optimizing the growth of semiconductor materials by combining
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and variation. The successful candidate will develop innovative methods and models to advance our understanding of genome evolution and variation. The position is based in the Computational Genomics
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and variation. The successful candidate will develop innovative methods and models to advance our understanding of genome evolution and variation. The position is based in the Computational Genomics
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, such as initiatives on model-based systems engineering in aircraft engine systems development. Possible research directions include: data-driven geometry assurance, tolerancing and variation simulation
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to use data and modelling to make historic buildings energy efficient – without compromising their cultural value? Join the research project DASHER at KTH. DASHER (Data-driven Structured Historic Energy