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PhD Studentship: Preclinical modelling and therapeutic targeting of glioblastoma infiltrative margin
PhD Studentship Advert Title: Preclinical modelling and therapeutic targeting of glioblastoma infiltrative margin Supervisors: Prof Ruman Rahman, Dr Stuart Smith, Dr Phoebe McCrorie Project Overview
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Sorbonne Universite - Laboratoire de Chimie de la Matière Condensée de Paris (LCMCP) | Paris La Defense, le de France | France | about 11 hours ago
developed a partial model of a healthy IVD that reproduces the spatial organization of the nucleus pulposus and partially that of the annulus fibrosus. The aim of this doctoral project is to further develop
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transfer; spatial neighborhood/domain analysis; multi‑omic modeling for RNA+protein where applicable. Pipeline automation & reproducibility – 10% Implement/maintain Snakemake/Nextflow workflows with
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fibroblast identity and function within liver tumours and how these cells shape anti-tumour immune responses. The student will use in vivo cancer models, spatial tissue analysis and immunological profiling
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Classification Title: PhD in Bioinformatics, Computational Biology, Biomedical Engineering, Data Science, or a related field. Classification Minimum Requirements: PhD in Bioinformatics
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vulnerability Single-cell and spatial transcriptomic analysis of treated organoids and tumour Computational modelling of cell state rewiring network Functional validation of identified targets in various tumour
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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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deglaciation. Duties include but are not limited to: The analysis will include statistical modeling (e.g., STEHME) to bridge spatial and temporal data gaps and interpolate beyond field-accessible regions. Using
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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