PhD Studentship in Bioinformatics: Applications are invited for a fully funded PhD studentship in Bioinformatics at the Science for Life Laboratory (SciLifeLab), Stockholm, Sweden. This interdisciplinary project focuses on developing AI-driven, perturbation-based approaches for gene regulatory network (GRN) inference using single-cell, spatial, and multi-omics data. The PhD will be jointly embedded within a strong computational and life science ecosystem at Stockholm University, KTH, and Karolinska Institutet, and supervised by Professor Erik Sonnhammer.
Fully Funded PhD Studentship in Bioinformatics: Perturbation-based Multi-omics Inference of Gene Regulatory Networks
Designation
PhD Student / Doctoral Researcher (Bioinformatics)
Research Area
- Bioinformatics
- Gene Regulatory Networks (GRNs)
- Multi-omics Data Integration
- Single-cell & Spatial Omics
- Artificial Intelligence / Deep Learning
- Systems Biology
- Cancer Systems Biology
Location
Science for Life Laboratory (SciLifeLab)
Stockholm, Sweden
(Joint research environment of Stockholm University, KTH Royal Institute of Technology, and Karolinska Institutet)
Eligibility / Qualification
Applicants must meet one of the following criteria:
- M.Sc. in Bioinformatics or a closely related field with strong molecular biology knowledge, OR
- M.Sc. in Molecular Biology (or related field) plus at least 1 year of documented practical experience in bioinformatics research and programming
Essential Skills
- Strong programming experience in Python, Matlab, and R
- Good working knowledge of UNIX/Linux
- Experience with omics data analysis (gene expression, chromatin accessibility, etc.)
Desirable / Meritorious Skills
- Experience with deep learning frameworks such as PyTorch or TensorFlow
- Familiarity with single-cell or spatial omics data
- Interest in AI-based biological modeling
Job Description
The PhD project aims to advance gene regulatory network inference by exploiting experimental perturbation designs in multi-omics datasets. Key objectives include:
- Developing novel AI and deep learning frameworks for perturbation-based GRN inference
- Leveraging perturbation information to improve GRN quality in single-cell and spatial multi-omics data
- Designing specialized neural network architectures to connect molecular data with specific gene perturbations
- Inferring perturbation designs directly from gene expression and chromatin accessibility data
- Applying the developed framework to spatial omics data to link GRNs with tissue phenotypes, including cancer development
The position offers access to excellent computational infrastructure and a vibrant, interdisciplinary research environment.
How to Apply
Interested candidates must follow the official application instructions available at:
👉 https://sonnhammer.org/download/ads/open.html
For project-specific queries, contact:
Prof. Erik Sonnhammer
📧 Erik.Sonnhammer@scilifelab.se
📞 +46-(0)70-5586395
🌐 http://sonnhammer.org
Last Date for Apply
February 22, 2026
