75 embedded-systems-"https:"-"https:"-"https:"-"https:"-"https:"-"UCL" PhD positions at University of Copenhagen
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Crop Ecology research group headed by Associate professor Dorte Bodin Dresbøll. Focus of the research group is on resilient and sustainable cropping systems with special emphasis on belowground
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. The aim of this project is to combine biophysical methods, model membrane systems, epithelial permeation assays, and advanced microscopy to provide new molecular insight into oral peptide delivery
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is embedded within the Multispecies Systems Microbiology Group at the Department of Food Science. The successful candidate will join an ambitious and collaborative research environment with access to
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sharing best practices, training colleagues, and fostering a collaborative learning environment. The position is embedded in an international and inclusive research setting that values teamwork
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innovation and scientific independence are encouraged. The group is embedded within ICMM, providing access to outstanding facilities for human and mouse embryology, advanced microscopy, genome engineering
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of care for the green transition. The position is embedded in the research project “INFRA-CARE: Nurturing Infrastructures of Care by Centering Culture, Nature, and Solidarity in the Green Transition
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information about the core group project is available here: https://teol.ku.dk/english/research/dept/akh/writing-with-the-dead/ . The Research Environment and Activities The Faculty of Theology is dedicated
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pharmaceutical systems. The team is part of the research group Pharmaceuticals, Processes and Products at the Department of Pharmacy. The team is engaged in the Center for Pharmaceutical Data Science
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, multi-omics analyses and advanced 2D and 3D cellular model systems to investigate the mechanisms underlying post-acute disease trajectories affecting the cardiovascular, pulmonary, and renal system
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infrastructure. Project description The PhD project will focus on developing intelligent AI systems, language language models (LLMs), and AI-for-Science approaches that can be integrated into the GPAP platform