This PhD position at Karolinska Institutet offers an opportunity to join the research group of Associate Professor Susanne Schlisio in the Department of Oncology-Pathology, located at Bioclinicum, Solna. The Schlisio laboratory focuses on understanding the molecular and cellular mechanisms that drive tumor initiation, progression, and therapy resistance in sympatho-adrenal cancers, particularly neuroblastoma and paraganglioma. The group employs advanced techniques such as single-cell and spatial transcriptomics, lineage tracing, and genetically engineered mouse models to dissect tumor plasticity, lineage hierarchies, and the developmental origins of cancer. The overarching aim is to translate fundamental discoveries into new strategies for precision oncology and patient stratification. The lab is well-funded, including support from an ERC Synergy Grant, and is part of a vibrant network of collaborations with SciLifeLab and the Swedish Childhood Tumor Biobank. The department provides a collaborative environment with strong links to translational cancer research and clinical applications at Karolinska University Hospital. The doctoral project, titled “Targeting malignancy in neuroblastoma and paraganglioma driven by cell plasticity using spatial transcriptomics and machine learning,” seeks to elucidate how tumor cell plasticity and microenvironmental signals contribute to malignancy, progression, and treatment resistance. The student will generate and analyze spatial and single-cell transcriptomic data from human tumors, integrate developmental reference datasets, apply graph-based and machine-learning tools to model signaling networks, and validate candidate pathways in cell culture and mouse models. The position offers comprehensive training in computational techniques, participation in doctoral courses, and engagement in international collaborations. Applicants must have a Master’s degree in a relevant quantitative field, strong programming skills in R and/or Python, and experience in data analysis or machine learning. Familiarity with RNA-seq analysis, data visualization, and computational workflows is required. Desirable qualifications include experience with single-cell or spatial transcriptomics, graph-based modeling, deep learning, Linux/Unix, Git, and high-performance computing. The position is a full-time, four-year doctoral studentship with a contractual salary and access to university facilities. Applications are to be submitted through the Varbi recruitment system by 10 December, including all required documentation. This is an excellent opportunity for a motivated and analytical individual to develop as an independent researcher at the forefront of cancer plasticity, single-cell biology, and precision oncology.