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following methods: microscopy, epigenome analysis, molecular biology techniques or working with Arabidopsis. documented knowledge in a relevant field of research capacity for analytical and creative thinking
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out together with seven industrial partners and is externally funded by the Knowledge Foundation. In co-production with our corporate partners and the community, we develop concepts, principles, methods
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collaboration and information sharing. You will formally be part of LOE’s Organic Bioelectronics group (https://liu.se/en/research/organic-bioelectronics ), led by Prof. Daniel Simon (https://liu.se/en/employee
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strategies studied in comparison with conventional remediation strategies. The project aims to develop and apply methods for assessing environmental impact, ecosystem services, and technical and socioeconomic
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Join MultiD Analyses AB and the University of Gothenburg to develop innovative bioinformatics and machine learning methods for RNA Fragmentomics, with the ambition to improve cancer care through
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qualitative and/or quantitative research methods. Experience with surveys, interviews, and/or statistical analysis. Proficiency in written and oral communication in English. Experience of collaboration with
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Are you interested in developing mathematically grounded methods for uncertainty quantification in deep learning, particularly for large language models in healthcare applications? Are you looking
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exclusion. The work duties also include some data analysis and compiling a methods guide. The data gathering requires local travel, while other tasks are completed on site at Lund University. Qualifications
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duration of the education, which corresponds to four years. Position description As a doctoral student, you will conduct research focusing on the development and application of AI-based methods for energy
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motion in non-classical geometries using combinatorial, algebraic, and group-theoretic methods, inspired by the mathematical theory of articulated systems and constraint geometry. Possible tools include