Join MultiD Analyses AB and the University of Gothenburg to develop innovative bioinformatics and machine learning methods for RNA Fragmentomics, with the ambition to improve cancer care through data-driven diagnostics.
About the project
Cancer treatment often involves surgery, chemotherapy, and radiotherapy. While these treatments have improved outcomes, they can be long-lasting and cause severe side effects. A major challenge is monitoring treatment efficacy in detail and detecting relapse early.
This project addresses that challenge by analyzing cell-free RNA in patient blood samples. These measurements can reveal disease status, how tumor cells respond to therapy, and whether treatment strategies should be adjusted, continued, or stopped.
The overall goal is to develop innovative bioinformatics methods for detailed analysis of different RNA molecules in blood samples and contribute to a new research field with strong clinical potential.
What you will work on
The successful candidate will be employed by MultiD Analyses AB and will split their time between MultiD at GoCo Health Innovation City and the University of Gothenburg, Gothenburg, Sweden. The academic supervisor is Professor Anders Ståhlberg. Co-supervisors at MultiD are Dr. Martin Smelik and Professor Mikael Kubista. The project will be conducted in close collaboration with partners within the academy, industry and health care.
Method Development
Design new statistical and machine learning models tailored to this emerging omics modality.
Multimodal Data Analysis
Work with high-dimensional datasets combining quantitative RNA features, positional fragment data, and clinical variables.
Real-world Biomedical Impact
Apply methods in ongoing biological and clinical studies with the ambition of implementation in healthcare.
Cross-sector Collaboration
Conduct research together with academic, industrial, and healthcare partners in a highly collaborative environment.
Who we are looking for
- A highly motivated PhD candidate excited by computational biology, biostatistics, bioinformatics, or data science.
- Someone eager to work at the intersection of data-driven life science, translational research, and cancer diagnostics.
- A candidate interested in methodological innovation as well as real biomedical applications.
- Applicants should include a personal letter and CV with information about programming skills.
Eligibility requirements
The position follows the doctoral education requirements for admission to third-cycle studies.
- Completed a degree at second-cycle level, or
- Completed course requirements totaling at least 240 credits, of which at least 60 credits are at second-cycle level, or
- Acquired equivalent knowledge in another way, in Sweden or abroad.
Specific requirement: Successful completion of English B/6 or equivalent knowledge through previous studies.
About the DDLS program
Data-driven life science (DDLS) combines data, computational methods, and artificial intelligence to study biological systems from molecular structures to human health and ecosystems.
The SciLifeLab and Wallenberg National Program for Data-Driven Life Science aims to recruit and train the next generation of data-driven life scientists and create globally leading computational and data science capabilities in Sweden.
In 2026, the DDLS Research School will be expanded with the recruitment of 25 academic and 7 industrial PhD students. During the course of the DDLS program, more than 260 PhD students and 200 postdocs will be part of the Research School. The DDLS program has four strategic research areas: cell and molecular biology, evolution and biodiversity, precision medicine and diagnostics, epidemiology and biology of infection.
For more information, please see https://www.scilifelab.se/data-driven/ddls-research-school/ The future of life science is data-driven. Will you be part of that change? Then join us in this unique program!