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Field
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Faculty of Health and Medical Sciences, University of Copenhagen We are offering a three-year PhD fellowship in image analysis using deep learning, commencing 1 December 2025 or as soon as possible
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as ProteinMPNN, RFdiffusion, and AlphaFold; prior hands-on use is beneficial but not required. Analytical chemistry: Experience or willingness to learn LC–MS, NMR, and UV–Vis for product and protein
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active sites), in vitro and in vivo enzyme screenings, electrochemistry, and machine learning-assisted directed evolution. As part of this project, you will collaborate closely with PhD students and
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, interdisciplinary, and international team Successful candidate should have prior experience in at least one of those areas: analytical methods (e.g. profilometry, SEM), machine learning and/or modelling, embedded
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applications Strong analytical and problem-solving skills Good communication skills and ability to collaborate across disciplines Motivation to make a real-world impact in sustainable energy and industrial
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on causal and mechanistic studies of microbiome-mediated pathogenesis. This is achieved by bridging microbiology and big data analytics in a structured doctoral training environment. The need of microbiome
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adapt advanced machine learning frameworks (SPARKS and CEBRA) for supervised and unsupervised analysis of high-dimensional neural data to decode multisensory information Investigate how neural
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. Knowledge on multiphase (gas-particle two phase system), thermal energy storage, and/or renewable hydrogen technologies. Familiar with application of machine learning and deep learning algorithms to fluid and
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off-the-shelf sensors and the development of resilient algorithms that combine first-principles modeling with modern machine learning techniques. The goal is to push the boundaries of robust perception
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of Science and Technology. MICRO-PATH addresses research questions based on causal and mechanistic studies of microbiome-mediated pathogenesis. This is achieved by bridging microbiology and big data analytics