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apply advanced statistical and computational methods to analyze genetic data generated from simulations, outputs from biophysical models, or empirical genomic data to answer questions in spatial
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quantify intraspecific and interspecific interactions, taking into account environmental, spatial and temporal variation. TASKS: The work is based on data analysis and modelling approaches. An initial phase
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for visa sponsorship. DESIRED QUALIFICATIONS Familiarity with the R-ArcGIS Bridge (arcgisbinding) and/or the ArcGIS API for Python and Esri's spatially enabled dataframes. Exposure to raster and statistical
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computational pipelines for the analysis of high-dimensional genomic and spatial-omics datasets. The successful candidate will work closely with experimental scientists to analyze complex biological datasets
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or Associate Professor level in the field of statistics or machine learning, with demonstrated interest in single-cell or spatial genomics applications. This faculty member will have a unique opportunity to
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, R, MATLAB) for data processing and analysis. - Experience in statistical processing of spatial and temporal data. 4) Instrumentation and metrology: - Knowledge of sensors and measurement instruments
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Full time, 3 years fixed term, located on the Camperdown Campus at the School of Mathematics and Statistics Join a world-class interdisciplinary team using AI, spatial omics and digital pathology
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, which may include working extended hours and weekends. Required qualifications: • Bachelor’s degree in forestry, natural resources, spatial sciences, emergency management, or related
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, and spatial learning. We want candidates who are eager to further develop as instructors, analysts, and applied practitioners while contributing meaningfully to curricular and co-curricular initiatives
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on single-cell and spatial genomics data, from sequencing and imaging, and apply them to human developmental and disease biology. Possible research directions include: ● Generative models of cell–cell