Karolinska Institutet is recruiting two PhD students to join a leading research group focused on developing machine learning (ML) and artificial intelligence (AI) models for radiological precision medicine, with a special emphasis on breast cancer diagnostics and health equity. The group, led by Fredrik Strand (Research Leader and Radiologist), is internationally recognized for its work in AI for breast cancer detection, with recent publications in top journals such as Lancet Digital Health and JAMA Oncology. The co-supervisor is Apostolia Tsirikoglou, the group's tech lead. The research group consists of around ten full-time members, including research specialists, postdocs, PhD students, and research engineers. The group collaborates with KTH, other groups at Karolinska Institutet, and international partners, including the University of California, Berkeley and San Francisco, and participates in two EU projects. PhD Project Tracks: Track 1: AI-image based risk models for personalized surveillance of individuals at genetic risk of breast cancer. This project integrates AI-derived imaging signatures from mammography and MRI with genetic information to develop and calibrate risk prediction models and evaluate risk-adapted surveillance strategies. Duties include training and evaluating AI models, quantitative risk modeling, and integrating imaging-based and genetic risk measures. Track 2: Equity and Disparities in AI-Supported Mammography Screening. This project examines fairness, equity, and generalizability of AI systems for breast cancer detection in population-based screening. The work involves evaluating AI performance across ethnic and socioeconomic subgroups, with opportunities for ML fine-tuning or fairness-aware model development, depending on the candidate's background. General duties for both tracks include contributing to study design, statistical analysis, interpretation of results, scientific writing, and dissemination at international conferences. PhD students will also take required courses and participate in activities included in the doctoral program. Eligibility and Requirements: Applicants must have a master's degree or equivalent in a relevant field, or have completed at least 240 credits (with at least 60 at the master's level), or possess equivalent knowledge. Proficiency in English equivalent to Swedish upper secondary school English B/6 is required. Necessary skills include experience with quantitative data analysis, statistical reasoning, proficiency in Python, strong analytical ability, and interest in interdisciplinary research at the interface of AI and medicine. Excellent written and spoken English and strong communication skills are essential. For Track 1, experience with medical imaging data, risk prediction, genetics, or machine learning is advantageous. For Track 2, background in epidemiology, public health, biostatistics, or related fields, and experience with population-based data, subgroup analysis, or fairness in ML models are desirable. Funding and Benefits: The position is a full-time doctoral studentship for up to 4 years, with a contractual monthly salary funded by the Swedish Research Council. Additional benefits include access to a modern gym and medical care reimbursements. Karolinska Institutet offers a creative and inspiring environment, international collaborations, and opportunities for international exchanges. Application Process: Applications must be submitted through the Varbi recruitment system. Required documents include a personal letter, CV, degree projects, publications, and documents certifying eligibility. Applications can be in English or Swedish. The deadline for applications is 14 January 2026. For more information, contact Fredrik Strand ([email protected]) or Apostolia Tsirikoglou. Join Karolinska Institutet and contribute to advancing AI in medicine and improving cancer care.

Karolinska Institutet is seeking a Research Assistant with a BSc or MSc in Biostatistics or Epidemiology to join a leading research group focused on developing and evaluating AI models for breast cancer risk prediction and radiological diagnostics. The group, led by Research Leader and Radiologist Fredrik Strand, is internationally recognized for its work in AI for mammography-based breast cancer diagnostics, with recent publications in top journals such as Nature and Lancet Digital Health. Collaborations include KTH, University of California, Berkeley, and University of Barcelona. The position is part of a project investigating fairness, equity, and generalizability of clinical AI systems for breast cancer screening. The main tasks involve linking retrospective mammography screening data with Swedish registries, ensuring data quality, contributing to statistical analysis plans, performing analyses in R and Python, and presenting results at meetings and conferences. The role also includes publishing at least one original scientific article and participating in weekly journal clubs to build a strong foundation for future doctoral studies. Applicants should have a degree in biostatistics, epidemiology, or a related field, with experience in quantitative data analysis and proficiency in R and Python. Strong English communication skills are required. Additional merits include experience in cancer screening, radiology, medical imaging (DICOM), and data extraction from non-SQL databases like MongoDB. The position is fully funded for approximately 12 months, with a monthly salary and benefits under the university's collective agreement, including flexible work arrangements and access to fitness facilities. The role is designed as a potential pathway to a PhD position. The application deadline is March 13, 2026, and applications should be submitted via the Varbi recruitment system with a CV and responses to application questions (no cover letter required). For more information, contact Fredrik Strand at [email protected] or visit the job posting link.