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Karolinska Institutet

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PhD in AI and Clinical Data Science for Breast Cancer Karolinska Institutet in Sweden

Degree Level

PhD

Field of study

Computer Science

Funding

Doctoral studentship employed for up to 4 years full-time, also possible part-time at a minimum of 50%. The position includes a contractual monthly salary under KI's doctoral student salary scale.

Deadline

Oct 20, 2026

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Country

Sweden

University

Karolinska Institutet

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Keywords

Computer Science
Biomedical Engineering
Information Technology
Biology
Artificial Intelligence
Medical Science
Breast Cancer
Clinical Data
Data Standards
Statistics
ML

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About this position

PhD opening at Karolinska Institutet in AI and clinical data science for breast cancer, based in the Department of Oncology-Pathology and the Computational Breast Imaging Group led by Fredrik Strand. The project sits at the intersection of computer science, biomedical engineering, medical science, machine learning, image analysis, data engineering, and clinical medicine.

The doctoral project develops agent-based data infrastructure and methods for transforming longitudinal, multimodal breast cancer data into structured clinical decision-state representations and benchmark datasets for evaluating AI-supported multidisciplinary decision-making. The work uses a large retrospective breast cancer cohort with more than 30,000 cases linked to the National Quality Register for Breast Cancer, and involves collaboration with clinicians and computer scientists.

The group is internationally recognized for AI in breast cancer imaging and precision medicine, with expertise in radiology, machine learning, and biostatistics. Infrastructure includes high-performance computing, GDPR-compliant secure storage, the VAI.B platform, and linked imaging, clinical, and registry data.

Eligibility highlights: a master's degree or equivalent in image analysis, machine learning, computer science, biomedical engineering, data science, or a related field; strong Python skills; experience with machine learning and/or image analysis; data pipelines or heterogeneous data sources; excellent English; and general doctoral eligibility plus English proficiency equivalent to English B/English 6. Desirable experience includes FHIR, OMOP, DICOM, multimodal or longitudinal data integration, Docker, reproducible research, trustworthy AI, and clinical translation.

The position is a doctoral studentship for up to 4 years full-time, also possible part-time at a minimum of 50%, with a contractual salary. Application is through the Varbi recruitment system. Deadline: 20 October 2026.

Funding details

Doctoral studentship employed for up to 4 years full-time, also possible part-time at a minimum of 50%. The position includes a contractual monthly salary under KI's doctoral student salary scale.

What's required

Applicants must have a master's degree or equivalent in image analysis, machine learning, computer science, biomedical engineering, data science, or a related field. Required skills include solid Python programming, experience with machine learning and/or image analysis, experience with data engineering or data pipelines and heterogeneous data sources, strong analytical ability, interest in interdisciplinary research at the intersection of AI and clinical medicine, ability to work independently and in a multidisciplinary team, and excellent written and spoken English. General doctoral eligibility and specific English proficiency equivalent to English B/English 6 are required. Desirable experience includes health data standards such as FHIR, OMOP, or DICOM, clinical data, multimodal or longitudinal data integration, Docker, reproducible research workflows, and interest in trustworthy AI and clinical translation.

How to apply

Apply through the Varbi recruitment system using the application button. Submit a personal letter, CV, degree projects and publications if any, other evidence of desirable skills, and documents proving general and specific eligibility. Application can be written in English or Swedish.

More information can be found here

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