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. Experience working with mobile air-pollution monitoring data or other high-frequency geospatial environmental measurements. Experience with spatial or spatiotemporal analysis, geospatial data
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experience in at least one of the following: The fields of Glycobiology, Inflammation, or Type 2 Diabetes Multiplexed staining methods Quantitative image analysis Spatial Omics Omics analysis Desirable
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Scientist (CS), PhD candidate – R1 Specialization and field of activity: Archaeology – History and archaeology of Roman Dacia: GIS methods and spatial analyses, photogrammetry and 3D modelling of
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site visits. This PhD project aims to leverage innovative spatial computing technologies and proposes Immersive Contextual Data Analytics (ICDA) as a method to address contextual analysis challenges by
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spatial statistics. Close the experimental-computational loop, using analysis results to guide the design of the next round of automated experiments (design of experiments, active learning). Ensure
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and environmental challenges. Candidates should demonstrate a strong foundation in spatial analysis and the ability to connect methodological development with substantive applications. Relevant
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development and how disruption of these processes contributes to congenital heart defects. The project will combine state-of-the-art single-nucleus RNA sequencing, spatial gene expression analysis and
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characterise population trends and quantify intraspecific and interspecific interactions, taking into account environmental, spatial and temporal variation. TASKS: The work is based on data analysis and
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as MERFISH/MERSCOPE, Visium HD and ATERA. Depending on your interests and strengths, you can choose to focus more strongly on either single-cell and perturbational data analysis or spatial data
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insights from human populations. We use single-cell and spatial genomics, epigenomic profiling, microbiome analysis, and large-scale human datasets to uncover biological principles in reproduction and