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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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criteria: Documented knowledge, preferably from his / her university education, is required in: mathematics, especially differential equations; numerical methods and computer programming; physical
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researchers, teachers, and other staff engaged in education and research in economic history, business administration, business law, informatics, economics, and statistics. The School of Economics and
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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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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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that they will actively and creatively develop and optimize the detailed methods to pursue the overall project goals. All work will be carried out embedded in a collaborative research team, requiring sharing
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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