The overall aim of the project is to identify new drug targets/drugs for glioblastoma (GBM), the most malignant and most frequent primary brain tumor, by combining breakthrough technologies from machine learning (ML), bioinformatics, single cell and spatial transcriptomics, organoids, and biotechnology. The person appointed on this position will use causal inference and ML algorithms to identify causal gene regulatory networks in GBM long-term survivors compared to short-term survivors and link these networks to specific tumor/stromal subpopulations by screening single cell/spatial transcriptomics data
Requirements:
Applicants must hold a master's degree or equivalent education in informatics, machine learning, bioinformatics, or related discipline, with several years of experience in a research environment; Experience with causal inference is a requirement; Experience with machine learning is a requirement; Experience of programming in at least one programming language is a requirement (Julia, Python, C, C++)
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