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spatial and temporal variability in sediment accumulation and vegetation development. This PhD project is part of a larger interdisciplinary research initiative aiming to enable a transition towards more
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involve analysis of complex biological and medical data including multi-omics, ex vivo drug screens, patient treatment histories, and spatial profiling of cancer patient tissues. Develop novel computational
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analytical or computational methods (e.g., remote sensing, spatial analysis, systems or integrated assessment modelling) applied to sustainability. Candidates from other methodological areas with clear
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) and spatial transcriptomics on CRC patient samples, we have identified novel epithelial cell states and distinct cancer-associated fibroblast (CAF) subpopulations. The postdoctoral fellow will work
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The Geeleher Lab tightly integrates computational/AI-based analysis of high-throughput genomics datasets (e.g. single-cell / spatial genomics, functional screens) with wet-bench experimental work
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evaluation framework covering statistical fidelity, temporal and spatial structure, physical plausibility and downstream task performance (e.g. train-synthetic-test-real forecasting). You will collaborate with
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Good knowledge of aquatic ecology Experience with geographical information systems and spatial analysis Experience with sociological survey design Experience with ecological field studies About us Do you
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the western United States. The project will provide hands-on experience in satellite remote sensing, geospatial analysis, wildfire science, and multi-sensor Earth observation, complementing the participant’s
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omics-based approaches. Interest in spatial biology, single-cell/spatial omics technologies and the integration of tissue-based, molecular and clinical data. Basic computational and data-analysis skills
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qualifications will be considered an advantage: Knowledge of C++ and C#. Experience with spatial transcriptomics and other spatial omics data analysis. Knowledge of advanced biostatistical methods, including