10 learning-"https:"-"https:"-"https:"-"https:" Postdoctoral research jobs at Aarhus University
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postdoctoral researcher to join an interdisciplinary team developing deep learning models for antimicrobial resistance (AMR) detection directly from MALDI-TOF mass spectrometry data. The project is funded
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of Prof. Georg Madsen, with regular shorter research stays at Aarhus University. The project combines density functional theory (DFT), machine-learned force fields and atomistic simulations to uncover how
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, extract, and standardise functional information 2. Develop computational tools that integrate evolutionary and functional information using comparative genomics and deep learning approaches 3. Apply
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: Overcoming Inequity in Embodied Learning in Danish Vocational Education and Beyond, funded by Independent Research Fund Denmark. This is a full-time (37 hours per week), fixed-term (24 months) postdoctoral
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independently with ample resources. This position offers a unique opportunity to combine your expertise in mouse metabolism with a willingness to learn and apply state-of-the-art mass spectrometry techniques, as
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data or large data volumes in all information systems. We contribute methods and algorithms for machine learning, and data mining, including XAI, as well as for data access and query processing. Aarhus
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environment, with regular scientific exchange and knowledge-sharing that helps lab members learn from each other and move projects forward efficiently. What we offer The Department of Molecular Biology and
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including bio-informatic processing To have experience with nucleic acid molecular manipulation incl PCR and experience in next generation sequencing -primarily on the illumina platform. To be eager to learn
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are motivated to learn EV and small-RNA methods are explicitly encouraged to apply. Required qualifications PhD degree in Plant Science, Plant Physiology, Plant Molecular Biology, Microbiology or a related
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developing new visualisation strategies to aid delineation, as well as developing deep learning methods to enhance photon-counting CT images and better visualise tissue boundaries. The project will also