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posi-tive samples, label uncertainty, the lack of negative labels, managing bi-ased information, and exploring appropriate evaluation metrics and strate-gies for gene prioritization models. For more
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clear advantage. Familiarity with spatial data analysis in R (GIS) is also an advantage. Applicants must be able to work independently and in a structured manner and demonstrate good collaborative skills
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, geography, spatial economics, computer science or related fields. You must have a professionally relevant background in conducting research or working with research projects. Advanced knowledge of GIS
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explore if, how, and under what conditions violence, conflict, and wars alter gender relations at multiple scales. Empirically, we combine surveys, experiments, archival data, GIS, and qualitative field
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breeding season is a clear advantage. A documented interest for fieldwork involving wild birds in Scandinavian weather is a clear advantage. Familiarity with spatial data analysis in R (GIS) is also an
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advantage. Familiarity with spatial data analysis in R (GIS) is also an advantage. Applicants must be able to work independently and in a structured manner and demonstrate good collaborative skills
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a condition of employment that the master's degree has been awarded. Experience from machine learning, geomatics/GIS and spatial statistics is a requirement. Good programming skills is a requirement
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uncertainty, the lack of negative labels, managing bi-ased information, and exploring appropriate evaluation metrics and strate-gies for gene prioritization models. Qualification requirements The Faculty
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-gies for gene prioritization models. Qualification requirements The Faculty of Mathematics and Natural Sciences has a strategic ambition to be among Europe’s leading communities for research, education