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cardiovascular disease (CVD) in Type 1 (T1D) and Type 2 (T2D) diabetes and obesity. This position is engaged in the field of Spatial transcriptomics and bioinformatics. This position supports the analysis
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persistence in chronic infections (Salmonella, Pseudomonas, and Achromobacter) by integrating spatial modelling, single-cell transcriptomics, advanced imaging data, and machine learning approaches. The goal is
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. Defrasne, have highlighted: (i) the value of interdisciplinary and multiscale analysis of rock surfaces, enabling the mapping of various natural and anthropogenic processes; (ii) the contribution
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several types of spatial growth patterns which correlate with therapy efficiencies and overall survival (Vermeulen et al. J Pathol. 2001 , Nielsen et al. Mod Pathol. 2014 , Baldin et al. J Pathol Clinical
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and established on-farm trials in Africa. Conduct spatial analysis to predict yield at scales. Contribute to the overarching goals of the research project team. Supervise master and doctoral student
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they continue to investigate. Approaches include genetics, spatial genomics, single-molecule biophysics, super-resolution imaging, computational modeling, and structural studies including X-ray
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communities • Multivariate statistical analysis of community and environmental datasets • Spatial analysis and georeferencing of ecological data using GIS • Development and implementation of species
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composition, healing potential, and immune responses. In-depth analysis will be performed with the state of the art sequencing techniques - single cell approach combined with short and long-read sequencing
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on complex problems involving the development of new theories and methodologies. The research will be largely focused on the development of predictive computational tools for the analysis of the spatial spread
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working knowledge of GIS platforms (e.g., ArcGIS, QGIS) and spatial data analysis techniques. Training or demonstrated experience in remote sensing, spatial data collection, and thematic mapping