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and Data Science for Spatial Genomics in Diabetes This position centers on the development and application of machine learning, image analysis, and integrative omics approaches to spatial
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monitoring data and new data collected within the project. Areas and time periods with different abundances of herring will be used to investigate the role of herring in the coastal ecosystem, as prey
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bioinformatics, with a particular emphasis on performing analysis of high-dimensional data, which can be sequencing and/or imaging-based. Experience working with AI and machine learning approaches are considered a
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-of-computing-science/ Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data-driven models for complex data, including high
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researcher will contribute to exploring how logistics and packaging innovations can improve food system preparedness and sustainability. The work integrates multiple tensions: the need for resilience versus
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environment project, we will develop automated species and community recognition, particularly focusing on pathogenic soil fungi, with help of deep-learning algorithms fed with microscopic image and Raman
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laboratory rearing conditions shape virus transmission https://www.umu.se/en/ucmr/ec-postdoc-programme/viromevector-competence-shifts-how-mosquito-laboratory-rearing-conditions-shape-virus-transmission/ 8
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the following is required and should be clearly described in the personal letter: Protein expression in various prokaryotes and/or yeasts Experience with multiple protein purification techniques Development
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combine large-scale data, computational methods, and clearly articulated social-science theories to improve our understanding of society. Recent advances in machine learning, natural language processing
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combine large-scale data, computational methods, and clearly articulated social-science theories to improve our understanding of society. Recent advances in machine learning, natural language processing