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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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designing and building visualization dashboards. Research experience in human-centered AI, or in the integration of AI and machine learning methods into interactive visualization and analysis systems. Strong
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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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intelligent control and aerial robotics for navigation in uncertain environment. You will be mainly responsible: for implementation of machine-learning algorithms for unmanned aerial vehicles; validation