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analysis Post-Doc experience in root exudate analysis and data evaluation using LC-MS/MS and GCMS/MS as well as various data evaluation tools and platforms Experience with different plant growth systems (e.g
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, quantitative image analysis) Computational skills in data processing and statistical analysis; experience with sc/snRNA-seq, NGS, metabolomics, proteomics datasets (R/Python) is highly desirable Demonstrated
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for the model, or novel applications of the model in research fields that we already cover (e.g., analysis of ice core data; transport of heat, water, greenhouse gases, radionuclides or air pollutants in
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enumerators during data collection and analyse the survey data. The quantitative postdoctoral researcher will also be involved in the analysis of the survey data of the SYREALITY project. Taking on a
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, or novel applications of the model in research fields that we already cover (e.g., analysis of ice core data; transport of heat, water, greenhouse gases, radionuclides or air pollutants in the atmosphere
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automation systems using machine learning techniques. Develop interpretable and explainable models to support root-cause analysis and actionable diagnostics. Contribute to peer-reviewed publications, open
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analysis of Arabic-language texts, media, and other relevant sources. The appointment will initially be made for 12 months and, subject to a positive evaluation, extended for an additional 44 months. Your
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or related disciplines. Outstanding dissertation. Proficiency in social science qualitative research methods. A keen interest in acquiring advanced skills in discourse analysis and social network analysis
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, Biochemistry, or similar) Experience working with a plethora of bioinformatics tools dealing with sequence and structure analysis. Excellent knowledge and experience in structural biology. Didactic competence