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PhD (m/f/d) position in the MSCA Doctoral Network “MobiliTraIN” on fundamental ion mobility research
, and data scientists, who daily take on the challenge of developing mass spectrometry instrumentation that can unravel fundamental properties of molecular systems. We have a strong network of
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Research FieldChemistryEducation LevelMaster Degree or equivalent Skills/Qualifications A MSc in Natural Sciences in a field relevant to natural product research. Skills in analytical chemistry (HPLC, Mass
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opportunity to learn first-hand a plethora of modern research methods including cryo-electron microscopy, x-ray crystallography, mass-spectrometry, molecular biology & biophysical techniques. The successful
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-edge metabolic/lipidomic assessment tool, including those applicable for single-cell characterization. The focus will lie on mass spectrometry imaging (MSI) and co-culture systems of healthy and tumor
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Universität Berlin (Germany) offers 2 PhD positions in the framework of the MSCA Doctoral Network MobiliTraIN (Ion Mobility Mass Spectrometry Training Network). The project aims to bring a new fundamental
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of this topic). We use a combination of genetic (CRISPR-Cas), biochemical, chemical-biology, and mass spectrometry-based approaches to mechanistically characterize protein quality control pathways in human cells
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kinase) by mass spectrometry. The most promising ligands will be optimised through further rounds of plate-based chemistry. To characterise optimised covalent ligands. We will harness a range of structural
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or identification of proteins by mass spectrometry. In this project you will synthesise and apply chemical tools to understand the mode of action of small molecules involved in host-microbe communications. The human
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blood cancer myeloproliferative neoplasms (MPN) (Jutzi and Marneth et al., 2022). This disease is driven by overactivation of thrombopoietin receptor MPL. Using mass spectrometry, you will generate a full
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(proteomics, metabolomics, Raman) Combining Raman spectroscopy and mass spectrometry-based proteomics, along with cutting-edge machine learning approaches, to enable timely identification of high risk for the