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analysis with spatial data to assess cascading supply chain risks and other systemic effects, supported where relevant by system dynamics modelling. Second, the PhD candidate will develop models to assess
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Experience in groundwater modeling. Strong skills and experience with GIS platforms (e.g., ArcGIS, QGIS) and spatial data processing. Skill and experience with machine learning. Have a publication track record
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E. coli cytochrome bd-I oxidase (2025) J. Chem. Inf. Model. 2026, 66, 3, 1757–1768 Where to apply Website https://emploi.cnrs.fr/Offres/Doctorant/UMR8000-FABCAI0-001/Default.aspx Requirements
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histological analysis (RNA-seq and spatial transcriptomics a plus). * Surgical Mastery (Critical): Substantial experience and strong proficiency in rodent stroke models (tMCAO/pMCAO), including stereotaxic
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-edge tools and machine learning techniques to model spatial multi-omics data. The aim is to advance our understanding of protein dynamics at the single-cell level and contribute to a broader
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funding calls. REQUIRED PROFILE • PhD in Hydrology, Environmental Science, Remote Sensing, Data Science, Civil Engineering, or a related field. • Strong background in hydrological modelling, time
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the advantages of the ADS are linked to its subcriticality, this must be ensured under all conditions (normal, incident, or accident). The SPATIAL project aims to develop a reliable method for measuring reactivity
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, preserving its mechanical properties is crucial. Tissue engineering (TE) and xenotransplantation, particularly using pig models, offer promising solutions. This project aims to advance meniscus TE and
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for high-dimensional datasets, including spatial bulk and single-cell RNA sequencing, mass and flow cytometry, and imaging data derived from both patient samples and experimental models. The overarching aim
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need a curious and driven PhD candidate to help us analyze these samples using spatial multi-omics. This is a joint project between Prof. Albena Jordanova (VIB-UAntwerp, Belgium) and Assist. Prof. Csilla