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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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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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tissue collection infrastructure, generate foundational single-cell and spatial omics datasets, and develop patient-tissue glioma organoid (PTGO) models to test immunotherapy strategies. The ultimate goal
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materials systems at the molecular level with machine learning. The PhD Student will work with tumour sections to develop multiple instance learning and weak supervision / spatial transcriptomics models
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. Our mission is to move beyond descriptive biology and develop predictive, mechanistic models that connect molecular regulation to cellular and systems-level phenotypes. The Laboratory of Computational
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. Our mission is to move beyond descriptive biology and develop predictive, mechanistic models that connect molecular regulation to cellular and systems-level phenotypes. The Laboratory of Computational
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technologies, spatial omics data, immunology and cancer systems biology. Experience with genomic engineering methods, stem cell culture, flow cytometry, and murine models is preferred. Ability to work
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approaches. The PhD will develop and apply optimization-based energy system models to analyse whether spatially coherent urban and energy configurations can be operated efficiently under realistic physical
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the molecular signatures of proteostasis loss and identify early markers of proteostatic failure. The role combines wet-lab spatial biology with computational approaches. You will work across models and scales
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particular, the research project will focus on inferring trajectories from spatial transcriptomics data modelling at the same time the cells evolution in gene expression and in space. Required skills : We