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Field
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data sources (e.g., registry data, surveys, and organisations). Your competencies Digital methods such as machine learning based classification, computational text analysis, network analysis, web
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research questions, and be able to think critically and develop your own scientific ideas. Previous experience with statistical analysis, programming (e.g., R or Python), machine learning, or genomic data
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Build, Division of Civil and Environmental Engineering, within the general study programme Civil Engineering and work with numerical modelling, time series analysis, and soil characterization
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Are you an experienced researcher in microbial genomics and bioinformatics with a strong record of university teaching, and expertise in whole-genome sequencing (WGS) analysis, machine learning and
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o calibration and/or data assimilation o satellite geodetic and/or Earth Observation (EO) data processing o meteorological/hydrological data processing and analysis Strong interest in collaborative
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PhD position in detector development for the next generation of cryogenic dark-matter axion exper...
RF components. Experience in cryogenics, superconductivity, data analysis, programming, and working in international research teams or collaborations will be considered an advantage. To read more about
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PhD Scholarship in University Students’ Learning Via Digital Twins of Neutron Scattering Instruments
studies and data analysis with one of the leading groups in the field. Qualified applicants must have: An MSc degree in Physics, Nanoscience, Chemistry, Engineering, or Material Science, or Educational
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integration and disease gene prioritization. Contributing to the translation of genetic discoveries into biological insight and precision prevention strategies. Your tasks will include data analysis, method
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collection and analysis is considered an advantage. Experience with case study research, interviews, observations, document analysis, or process-oriented research designs will be particularly relevant. We
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-computer interaction, social networks, fairness, and data ethics. Our research is rooted in basic research and centres on mathematical models of the physical and virtual world, as a basis for the analysis