493 model-driven-development "Integreat Norwegian Centre for Knowledge driven Machine Learning" positions in Sweden
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Join an international research environment developing the software infrastructure needed for next-generation fusion modelling. You will work at the interface between scientific computing, high
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scholarship in Medical Science with a focus on molecular mechanisms of MLL1-driven leukaemia. The scholarship is full-time studies for two years, beginning 1st of January 2027 or by agreement. Application
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: Experience in atomic scale modeling Experience from modeling reaction kinetics Experience in python programming What you will do Perform research in the project Develop your own scientific concepts and
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engineering in aircraft engine systems development in SIJET. Possible research directions include: data-driven geometry assurance, tolerancing and variation simulation automated generation of simulation models
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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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research in data-driven nutrition, health, and food science. With large-scale diet and health data, omics data, biomarkers, digital food and health services, we establish predictive models for evaluation
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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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environment focused on physics‑informed AI tools for materials design and characterization. In a collaborative project with industrial partners, you will work on developing physics‑informed models for heat
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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
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and AI methods for materials discovery. Design and implement active-learning workflows for autonomous exploration of materials spaces. Develop and evaluate generative AI models for inverse materials