Susanne Schlisio

Karolinska Institutet in Solna, Sweden, is offering a fully funded PhD position in Computational Cancer Biology within the research group of Associate Professor Susanne Schlisio at the Department of Oncology-Pathology. The Schlisio lab investigates the molecular and cellular mechanisms underlying tumor initiation, progression, and therapy resistance in sympatho-adrenal cancers, such as neuroblastoma and paraganglioma. The doctoral project focuses on targeting malignancy in neuroblastoma and paraganglioma driven by cell plasticity, using advanced computational and systems biology approaches, including spatial transcriptomics, single-cell RNA sequencing, and machine learning. The student will generate and analyze large-scale transcriptomic data, reconstruct differentiation trajectories, and model signaling networks driving tumor plasticity. The project aims to translate fundamental discoveries into new concepts for precision oncology and patient stratification. The lab is well-funded, with support from national and international grants, including an ERC Synergy Grant, and is part of a collaborative network with SciLifeLab and the Swedish Childhood Tumor Biobank. The position offers comprehensive training in computational techniques, participation in doctoral courses, and opportunities for international collaboration. 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, data visualization, and computational workflows is required. The position is salaried for up to four years, with additional benefits such as free gym access and medical care reimbursements. The application deadline is December 27, 2025. Applications must be submitted via the Varbi recruitment system, including all required documents as specified in the call.

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.