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evolutionary analysis. A central component of the research will be to develop machine learning and deep learning methods trained on coding sequences and protein structure to extract patterns in data and to draw
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part of an ERC starting grant and involves studying the impact of disease on endosomal properties and the processing of lipid nanoparticles. Key techniques will include omics, cell culture and small
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focused on mass spectrometry and development of new techniques for mass spectrometry imaging and single cell mass spectrometry to reveal chemical processes of importance to biological function and
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maintenance of instruments and computer systems, as well as assisting researchers in preparation of EM samples, data collection, and image analysis. You will also act as a coordinator for ongoing facility
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maintenance of instruments and computer systems, as well as assisting researchers in preparation of EM samples, data collection, and image analysis. You will also act as a coordinator for ongoing facility
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and/or functional imaging or application of computational modeling, machine learning and AI to understand cellular function. At least five years’ experience working within the university system, another
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spatial mass spectrometry. Experience with single-cell omics is also an advantage. Advanced biostatistics and machine learning, such as multivariate analysis, regularization, deep learning, or network
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backgrounds: Molecular biology, protein engineering, biochemistry. Optical engineering, fluorescence microscopy, image analysis: Development of microscopes and data analysis pipelines used to acquire and
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, e.g git. Experience of Linux/UNIX-terminal. Excellent communication- and collaboration skills. You speak and write English fluently. Meriting Experience of HPC or corresponding computer systems
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fluorescence microscopy (from single-molecule imaging to intravital microscopy), electrophysiology, respirometry, microfluidics, organoid cultures, bioprinting, and excellent opportunities to work with various