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of molecular and biological matter 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
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) data. We also analyse macaque electrophysiology data obtained through collaborations. We use machine learning techniques for data analysis and computational modelling with a special interest in
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analysis software (e.g., Fiji/ImageJ). Advanced Cell Manipulation (e.g., genome editing via CRISPR/Cas9, flow cytometry/FACS). Basic familiarity with data analysis tools (e.g., GraphPad Prism, R, or Python
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applications of advanced spatial data analysis methods, data collection, and interpretation of remote sensing data, database system theory, mapping, spatial data distribution, development of new cartographic
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these services, they create automated methodologies for system analysis, threat detection, and mitigation alongside privacy-enhancing technologies and data protection techniques. Collaborating with industry
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-scale data analysis with detailed contextual (often qualitative) data. You may have a disciplinary background in either social sciences (such as sociology, political science, communication sciences
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literature analysis, focusing on disentanglement, AI, XAI, and human-centric data analysis. · Elaborate validation use-cases, designing and planning data collection studies under controlled conditions
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interviews with farmers. - To adapt and validate a psychosocial questionnaire. - To collect quantitative data by repeated cross-sectional studies with farmers. To carry in-depth analysis of qualitative and
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learning and machine learning for biological data Sequence and structure analysis of large-scale datasets Functional annotation and evolutionary analysis Collaborative research with experimental virology
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: Quantitative analysis of experimental data and description of spatial structures in crowds (e.g., Minkowski functionals, Voronoi analyses, clustering methods) Comparison of physical structural analyses with