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UiO/Anders Lien 4th October 2026 Languages English English English 3-years PhD position in probabilistic machine learning and statistics Apply for this job See advertisement About the position We
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School. Responsibilities will span all stages of research, including collecting data in both tabular and spatial formats, developing algorithms that clean and organize data, conducting statistical analyses
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advanced biostatistics Computer proficiency, including use of statistical software for spatial analyses, such as ArcGIS, QGIS, R, Google Earth Engine Experience assembling, organizing, and analysing large
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key research questions that crosscut the themes of statistics, graphical models, causal inference, active learning, spatial modeling, and deep generative models. Execute multiple projects that will
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applying customized Bayesian statistical models for wildlife management and monitoring data. Learning how spatial and ecological modeling approaches are used to study wildlife disease spread and management
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-cell RNA-seq) Preferred Qualifications Background in statistical or population genetics, including variant annotation and prioritization Experience with single-cell or spatial transcriptomics Familiarity
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systems Working knowledge of ecological (especially landscape ecology) research methodology and advanced biostatistics Computer proficiency, including use of statistical software for spatial analyses
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with an interest in developing novel methodology that complements our strengths in spatial and spate-temporal statistics, AI, and data science with substantive application interests (e.g., precision
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(Apptainer/Singularity, Docker), and version control (Git). Experience with single-cell and spatial transcriptomics analysis. Working knowledge of applied statistics relevant to genomics, including generalized
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of multiplex cytometry data analysis programs including OMIQ Experience in statistical programming with R and/or Python. Knowledge of R/Python spatial transcriptomics data analysis packages such as Seurat/Giotto