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at the intersection of numerical analysis, uncertainty quantification, and scientific machine learning. The research will primarily focus on probabilistic methods for data-driven model reduction, with
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, REddyProc) Excellent numeric and analytical problem-solving skills, including time series analysis and statistics (e.g. mixed effects modelling) Capacity to develop computer code and experience with
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molecular structures capable of transferring electrons and interacting with light. Such assemblies also have applications in biomedicine. The primary objective is to develop computational methods, using deep
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methods and sample-preparation strategies; (iii) interpretable multivariate or ML models for classification and feature discovery; and (iv) high-impact publications and open, reusable analysis workflows
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several industrial and academic partners. The candidate will work with high-performance computing resources (NAISS) and advanced CFD tools, primarily OpenFOAM, with the possibility to use Nek5000 or LBM
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cutting-edge methods, models and technologies in environmental science, quaternary sciences, bedrock geology, paleontology, physical geography, biodiversity and ecosystem science, remote sensing, Geographic
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cluster is part of The Wallenberg AI, Autonomous Systems and Software Program – Humanity and Society (WASP-HS), which is a national research program in Sweden. The vision of WASP-HS is to foster novel
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agricultural contexts. Grounded in political ecology and actor-network theory, the project employs a mixed methods approach, integrating actor-network mapping, interviews, extended case studies and participatory
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group – lead by professor Sophia Zackrisson – with a main research interest in innovative imaging modalities and methods in breast cancer diagnostics, focusing on screening and the role of Artificial
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strategies. The research group focuses on exploration of tumor immune microenvironments through spatial omics and imaging, development of computational models for prediction of molecular and clinical features