Doctoral Student in Multiomics Machine Learning Cancer Bioinformatics
Join KTH to investigate cancer progression and tumor microenvironments using machine learning and multi-omics data integration.
Best for:Best for computational biology and machine learning graduates with a strong interest in cancer bioinformatics and multi-omics data analysis.
Overview
The KTH Royal Institute of Technology invites applications for a fully-funded doctoral student position in Cancer Bioinformatics within the School of Engineering Sciences in Chemistry, Biotechnology and Health. Based at SciLifeLab in Stockholm, the researcher will focus on precision medicine and bioinformatics-driven analyses to understand tumor progression in genitourinary cancers. The candidate will employ machine learning for multi-omics data integration while collaborating closely with clinical and translational research teams. The successful applicant is expected to conduct independent research, publish findings, and contribute to the lab's scientific goals.
What you'll do
- Perform bioinformatics-driven analyses to study tumor progression mechanisms in genitourinary cancers.
- Develop and apply machine learning algorithms for integrative multi-omics data analysis.
- Collaborate with clinical and translational researchers on cancer-focused computational projects.
- Manage and analyze large-scale biological datasets utilizing High-Performance Computing resources.
- Present research findings at scientific conferences and contribute to peer-reviewed publications.
- Contribute to departmental tasks such as limited teaching or administrative duties up to twenty percent.
Requirements
Degree required: Master's degree in bioinformatics, Computational Biology, or related fields
Degree offered: PhD
Language: English; English B/6 equivalent proficiency
Instruction: English
- Proficiency in programming languages such as Bash, R, and/or Python
- Demonstrated academic background or strong knowledge in cancer biology
- Experience with data analysis and multi-omics data integration
- Ability to work independently and collaborate effectively within multidisciplinary teams
- Strong problem-solving abilities and acute attention to detail
- Passed a second-cycle degree like a master's degree
- Completed higher education credits of at least 240 ECTS, with at least 60 credits at the second cycle
- Acquired substantially equivalent knowledge through other domestic or international pathways
Funding & terms
How to apply
Interested candidates should review the official KTH portal link and submit their complete application package including academic credentials and reference letters before the review process begins.
- CV
- Master's degree transcripts
- 2 reference letters
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