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Field
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for the analysis of stable carbon isotopes in methane using the Picarro system. You will collect quantitative data for MPn in water samples from the Baltic Sea and the Warnow Estuary in order to investigate its
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3 Dec 2025 Job Information Organisation/Company Helmholtz-Zentrum für Umweltforschung - UFZ Research Field Environmental science Researcher Profile First Stage Researcher (R1) Country Germany
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production of bacteria via mass spectrometry Statistical analysis of the resulting data The successful candidate will have the opportunity to work towards a PhD Required qualifications: University degree (M.Sc
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of provenance tracking within the workflow to enable reproducible data analysis, export to mzTab-M format Integration with EMBL-EBI BioStudies and other relevant repositories, such as MetaboLights (metabolomics
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sorting and data analysis Generation of single cell libraries with 10X Genomics, Singleron and BD Rhapsody platforms for ATAC, transcriptomics and multiomics assays Quality control of cells and nuclei (e.g
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machine learning tools for the efficient analysis of the experimental data. For more information, visit our web page www.soft-matter.uni-tuebingen.de We are looking for a motivated PhD student to contribute
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academic performance. Experience with stochastic methods, risk and reliability analysis, and data analysis. Programming experience in Python, MATLAB, C/C++, or a similar language. Strong analytical
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, including quality control, maintenance and troubleshooting on an Aria Fusion cell sorter Sample preparation, cell sorting and data analysis Generation of single cell libraries with 10X Genomics, Singleron and
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Deutsches Zentrum für Neurodegenerative Erkrankungen | Ulm, Baden W rttemberg | Germany | 13 days ago
in vitro Development of user-friendly tools for data analysis and tracking of experiments Prepare and write manuscripts for publication in international scientific journals Oral presentation of results
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using X-ray and neutron scattering. One of the research areas is the development of machine learning (ML) based approaches to efficient analysis of the vast data amounts generated in the scattering