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KTH Royal Institute of Technology

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Postdoc in Multi-Omics Machine Learning and Computational Cancer Biology KTH Royal Institute of Technology in Sweden

Degree Level

Postdoc

Field of study

Computer Science

Funding

Available

Deadline

Nov 15, 2026

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Country

Sweden

University

KTH Royal Institute of Technology

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Keywords

Computer Science
Biomedical Engineering
Biostatistics
Medical Imaging
Deep Learning
Biology
Precision Medicine
Medical Science
Oncogenomics
Omics
Statistics
Bioinformatic
computational cancer biology
ML

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About this position

KTH Royal Institute of Technology is offering a postdoctoral position in multi-omics machine learning and computational cancer biology within the A Lundberg Lab, which is part of the SciLifeLab and Wallenberg National Program for Data-Driven Life Science (DDLS). The lab is based at SciLifeLab Stockholm and KTH and focuses on computational approaches for cancer precision medicine.

The project centers on understanding the molecular mechanisms behind cancer aggressiveness, metastasis, and treatment resistance. The research combines cancer biology, multi-omics, and computational methods in an interdisciplinary environment with a strong publication record and a clear translational focus.

The successful candidate will work on integrating multiple data modalities, including biological data, clinical/pathological data, and medical imaging, with an emphasis on developing interpretable and biologically meaningful models. Methods highlighted in the advert include machine learning, deep learning, image analysis, computational pathology, multi-omics integration, and foundation models.

The position is aimed at a researcher with strong computational expertise and an interest in applying advanced methods to biomedical questions in cancer. It also offers opportunities to contribute to grant applications, research networks, education, and mentorship.

The environment is centered on interdisciplinary research at KTH and SciLifeLab, with links to the lab’s research profile and the broader KTH academic setting.

Funding details

Available

What's required

A doctoral degree in computer science, machine learning, computational biology, bioinformatics, or a related field is required and must be completed by the appointment decision. Applicants should have a strong research profile in machine learning, deep learning, computational biology, or related computational fields, with interest in cancer or biomedical research. Required skills and experience include programming in Python and/or R, developing and evaluating computational or machine learning methods, solid knowledge of statistics, biostatistics, and multivariate data analysis, excellent written and spoken English, experience with large-scale high-dimensional biological or biomedical datasets and heterogeneous data integration, one or more of multi-omics integration, multimodal machine learning, medical imaging, computational pathology, image analysis, biological data integration, or foundation models, Unix/Linux and high-performance computing, and scientific writing/publications and/or grant applications. Preferred qualifications include a doctoral degree obtained within three years of the deadline, experience with interpretable or explainable ML, cancer biology/genomics or precision medicine, large public or clinical datasets and independent project development, teaching/mentoring/supervision, strong analytical and problem-solving skills, collaborative interdisciplinary work, and a structured working style.

How to apply

Log into KTH’s recruitment system and submit the complete application online. Include a CV, copies of diplomas and grades, translations if needed, and a brief motivation letter of up to two pages describing your interest in A Lundberg lab and how it relates to your background and goals.

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