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design. You will be working alongside internationally recognized scientists who are leaders in such disciplines as AI/machine learning (Dr. M Madan Babu), NMR (Dr. Charalampos Kalodimos), cryo-EM/ET (Dr
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-throughput data analyses, machine learning and software engineering. We work in a highly dynamic and collaborative environment focussing on the identification of molecular markers of breast cancer by
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the ability of neural networks to learn unknown posterior distributions distributions. Their use in the field of image microscopy, however, remains limited. The purpose of this PhD thesis is to develop
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. Its human size fosters close interaction between students and lecturers, creating a personal atmosphere. With 1,600 members of academic staff for 6,700+ students and 1,000+ PhD candidates, students feel
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Programme is a scientific visitor scheme designed to provide early-career students/researchers (prior to embarking on a PhD) with a passion for technology and tool development an opportunity to gain hands
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to design and implement or use generative models targeting RNA, DNA, and cellular systems, Computational biology, to develop/use the latest machine learning strategies and computational analytics to analyze
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with expertise in bioinformatics, machine learning, data integration & harmonization, natural language processing including large language models, imaging informatics, and pathology informatics are
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Devices (QCD) group at the Department of Applied Physics. In this position you have a chance to make history by demonstrating some of the first experiments of the future quantum-computer technology that is
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Associate Professor or DTU Tenure Track Assistant Professor (junior group leader) in High-through...
) to in-field testing of up to 800 strains. The scale and standardized approach will create a unique foundation for advanced data analysis, including AI, machine learning, and statistical modeling, aimed
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Associate Professor or DTU Tenure Track Assistant Professor (junior group leader) in Fermentation...
) to in-field testing of up to 800 strains. The scale and standardized approach will create a unique foundation for advanced data analysis, including AI, machine learning, and statistical modeling, aimed