The Department of Cell and Molecular Biology (CMB) is a department with a strong focus on basic
science. CMB conducts research and education in cell biology, molecular biology, developmental
biology, stem cell biology, cancer and infection biology. CMB is located in Biomedicum, a
interdisciplinary research center designed to concentrate much of the experimental research conducted at Karolinska Institutet Campus Solna under one roof to promote collaboration. Biomedicum houses approximately 1,200 researchers and other personnel and allows expensive equipment to be shared and utilized more effectively. The Llorens laboratory offers a vibrant, stimulating and international environment. Our lab uses deep learning models combined with single cell and spatial genomics technologies to understand injury and repair mechanisms in the mammalian central nervous system (CNS)
The research assistant will be an active member of a multidisciplinary team developing a computational and experimental platform for the optimization of gene therapy for CNS repair. The project will generate high-impact genomics datasets, computational tools, and experimental platforms with strong translational and (pre-)clinical relevance.
Your responsibilities
- Analyze single-cell and spatial multiome datasets (transcriptomics and epigenomics) from mouse
models and human patient samples to study gene regulation underlying injury response mechanisms
- Support the development and application of deep learning models to predict activity and specificity of gene therapy constructs
- Collaborate with experimental researchers to link computational predictions with genomics
validation strategies
We are recruiting an enthusiastic and highly motivated research assistant with a strong interest in
computational genomics and gene therapy.
Qualifications:
- MSc degree in bioinformatics, computational biology, biotechnology, or related
- Demonstrated experience in the analysis of single-cell and/or spatial genomics datasets (scRNA-seq, scATAC-seq, multiome)
- Proficiency in R and/or Python for data analysis and visualization
- Familiarity with gene regulation, epigenomics, and enhancer biology is desirable
- Interest in machine learning-driven approaches applied to biological data (prior experience is a plus but not mandatory)
- Excellent written and spoken English
A creative and inspiring environment with wide-ranging expertise and interests. Karolinska Institutet is one of the world's leading medical universities. Our vision is to pursue the development of knowledge about life and to promote a better health for all. At Karolinska Institutet, we conduct successful medical research and hold the largest range of medical education in Sweden. Karolinska Institutet is also a state university, which entitles you to several good benefits through our collective agreement. And you get to practice freely in our modern wellness facilities, where trained staff are on site.
Location: Stockholm
Link to our latest publication: https://www.nature.com/articles/s41593-025-02131-w
Link to our lab page: https://ki.se/en/research/research-areas-centres-and-networks/research-groups/ systems-regenerative-neurobiology-enric-llorens-group
KI applies individual and differentiated salary setting in accordance with our collective agreements RALS and RALS-T.
The application is to be submitted through the Varbi recruitment system.In this recruitment, you will apply with your CV without a personal letter. Instead, you will answer some questions about why you are applying for the job in the application form.
Anställningsform: särskild visstidsanställning | Anställningens omfattning: heltid | Antal lediga befattningar: 1 | Sysselsättningsgrad: 100% | Ort: Stockholm | Län: Stockholms län | Land: Sweden | Referensnummer: STÖD 2-3086/2026 | Kontakt: Enric Llorens , Enric Llorens , Margherita Zamboni , Margherita Zamboni , | Facklig företrädare: Björn Andersson, SACO , Björn Andersson, SACO , Bodil Moberg , OFR/ST , Bodil Moberg , OFR/ST , Elizabeth Valenzuela, SEKO , Elizabeth Valenzuela, SEKO , | Publicerat: 2026-07-09 | Sista ansökningsdag: 2026-08-31
