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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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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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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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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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: 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
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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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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