Postdoc in multi-omics machine learning and computational cancer biology

Job Description

The lab is part of the SciLifeLab and Wallenberg National Program for Data-Driven Life Science (DDLS) and is based at SciLifeLab Stockholm and KTH. We develop and apply computational approaches for cancer precision medicine , with a particular focus on understanding the molecular mechanisms underlying cancer aggressiveness, metastasis and treatment resistance. The research combines cancer biology, multi-omics and computational methods, with a strong track record of interdisciplinary research and publications in leading journals. More information about the lab is available at: A Lundberg Lab.

We will integrate multiple data modalities (biological, clinical/pathological data and medical imaging), with an emphasis on developing interpretable and biologically meaningful models . Methods include machine/deep learning, image analysis, computational pathology, multi-omics integration and foundation models .

The position is suitable for a researcher with a strong computational background and demonstrated expertise in machine learning, deep learning or related areas , combined with an interest in cancer biology and biomedical applications.

The position also includes opportunities to contribute to grant applications, research networks, education and mentorship.

Qualifications

Requirements

• A doctoral degree in computer science, machine learning, computational biology, bioinformatics, or a related field. This eligibility requirement must be fulfilled by the time the appointment decision is made.
• A strong research profile in machine learning, deep learning, computational biology, or related computational fields, with a demonstrated interest in applying computational methods to cancer or other biomedical research.
• Programming skills in Python and/or R, and experience developing, implementing, and evaluating computational or machine learning methods.
• Solid knowledge of statistics, biostatistics, and multivariate data analysis, including appropriate methods for model evaluation and validation.
• Excellent written and spoken English, as English is used in day-to-day work.
• Experience in: Working with large-scale, high-dimensional biological or biomedical datasets and integrating heterogeneous data types.
• One or more of the following areas: multi-omics data analysis and integration, multimodal machine learning, medical imaging, computational pathology, image analysis, biological data integration, or foundation models.
• Unix/Linux environments and high-performance computing systems.
• Scientific writing, scientific publications, and/or grant applications.

Preferred qualifications

• A doctoral degree obtained within the three years preceding the application deadline.
• Experience in: Developing interpretable or explainable machine learning models.
• Applying machine learning to cancer biology, cancer genomics, or precision medicine.
• Working with large-scale public or clinical datasets and independently developing research projects.
• Teaching, mentoring, or supervision.
• Well-developed analytical and problem-solving skills.
• Ability to work effectively in an interdisciplinary and collaborative research environment.
• A structured and organized working style.
• Awareness of diversity and equal opportunities, with a particular focus on gender equality.
• Great emphasis will be placed on personal skills.

Join us at KTH

KTH shapes the future through education, research and innovation. As a leading international technical university, we play an active role in advancing the transition towards a sustainable society. At KTH, you have the opportunity to grow and develop in a creative and dynamic environment, with good working conditions and attractive benefits. Equality, diversity and equal opportunities are essential to quality and form an integral part of KTH’s core values as a university and public authority.

Learn more about our benefits and what it's like to work and grow at KTH.

Trade union representatives

Contact information to   trade union representatives.

To apply for the position

Log into KTH's recruitment system to apply for this position. You are responsible for ensuring that your application is complete according to the instructions in the ad.

The application must include

• CV including relevant professional experience and knowledge.
• Copy of diplomas and grades from your previous university studies. Translations into English or Swedish if the original documents have not been issued in any of these languages.
• Brief description of why you want to conduct research in A Lundberg lab, your academic interests and how they relate to your previous studies and future goals. Max two pages long.

Your complete application must be received at KTH no later than the last day of application, midnight CET/CEST (Central European Time/Central European Summer Time).

About the employment

The position offered is for, at the most, two years.

A position as a postdoctoral fellow is a time-limited qualified appointment focusing mainly on research, intended as a first career step after a dissertation.

Other information

For information about processing of personal data in the recruitment process.

It may be the case that a position at KTH is classified as a security-sensitive role in accordance with the Protective Security Act (2018:585). If this applies to the specific position, a security clearance will be conducted for the applicant in accordance with the same law with the applicant's consent. In such cases, a prerequisite for employment is that the applicant is approved following the security clearance.

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Disclaimer: In case of discrepancy between the Swedish original and the English translation of the job announcement, the Swedish version takes precedence.

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Type of employment

Temporary position

Contract type

Full time

Full-time equivalent

100 %

First day of employment

According to agreement

Salary

Monthly salary

Number of positions

1

Location

Stockholm

County

Stockholms län

Country

Sweden

Reference number

PA-2026-3456

Published

26.Oct.2026

Last application date

15.Nov.2026

Contact

Arian Lundberg

arianl@kth.se