Two PhD Students in Machine learning for radiological precision medicine

Do you want to contribute to top quality medical research?

To be a doctoral student means to devote oneself to a research project under supervision of experienced researchers and following an individual study plan. For a doctoral degree, the equivalent of four years of full-time doctoral education is required.

The research group

The research group is led by Fredrik Strand, MSc MD PhD, who has a mixed background of medicine, engineering and economics, and is a breast radiologist at the Karolinska University Hospital. Co-supervisor is Apostolia Tsirikoglou, MSc PhD, who is the tech lead of the group. In total, there are around ten full-time members: research specialist, post doc, PhD students, and research engineers. Our research is internationally recognized for the development and evaluation of AI for radiological breast cancer diagnostics with recent publications in Lancet Digital Health, JAMA Oncology, Radiology, MICCAI, among others. Our group has expertise in radiology, biostatistics and machine learning. We have a strong collaboration with research groups at KTH, at KI, and internationally within two EU projects and a new collaboration with a group at the center for Computational Precision Health at University of California Berkeley and San Francisco.

The doctoral student project and the duties of the doctoral student

Track 1: AI-image based risk models to predict and personalize surveillance for individuals at genetic risk of breast cancer

This PhD project focuses on early identification and personalized surveillance of women at hereditary or polygenic risk of breast cancer, integrating AI-derived imaging signatures from mammography and MRI with genetic information.

The work includes development and training of AI models for risk prediction, quantitative risk modelling and calibration, and evaluation of risk-adapted surveillance strategies, with strong clinical anchoring and translational relevance.

Duties of the doctoral student will focus on training and evaluation of AI models using mammography and breast MRI, quantitative risk modelling and calibration, and integration of imaging-based AI risk with genetic risk measures to support individualized surveillance strategies.

Track 2: Equity and Disparities in AI-Supported Mammography Screening

This PhD project examines fairness, equity, and generalizability of AI systems used for breast cancer detection and risk prediction in population-based screening. The position is funded by the Swedish Research Council.

The project combines population-based performance evaluation across ethnic and socioeconomic subgroups with opportunities for machine learning fine-tuning or fairness-aware model development when the doctoral student has the relevant technical background. The aim is both to identify disparities and, where methodologically justified, to evaluate strategies to reduce them while maintaining clinical performance.

Duties of the PhD student will focus on evaluation of AI performance and equity across population subgroups using registry-linked data, with optional involvement in model retraining or fine-tuning aimed at reducing identified disparities, depending on the doctoral student’s background.

General duties for both tracks

For both tracks, the doctoral students are expected to contribute to study design, statistical analysis, interpretation of results, scientific writing, and dissemination of findings at international conferences. In addition, the PhD student will take required courses and other activities included in a PhD study program.

What do we offer?

A creative and inspiring environment full of expertise and curiosity. Karolinska Institutet is one of the world's leading medical universities. Our vision is to pursue the development of knowledge about life and to promote a better health for all. At Karolinska Institutet, we conduct successful medical research and hold the largest range of medical education in Sweden. As a doctoral student you are offered an individual research project, a well-educated supervisor, a vast range of elective courses and the opportunity to work in a leading research group. Karolinska Institutet collaborates with prominent universities from all around the world, which ensures opportunities for international exchanges. You will be employed on a doctoral studentship which means that you receive a contractual salary. Employees also have access to our modern gym for free and receive reimbursements for medical care.

Eligibility requirements for doctoral education

In order to participate in the selection for a doctoral position, you must meet the following general (A) and specific (B) eligibility requirements at latest by the application deadline.

It is your responsibility to certify eligibility by following the instructions on the web page Entry requirements (eligibility) for doctoral education.

A) General eligibility requirement

You meet the general eligibility requirement for doctoral/third-cycle/PhD education if you:

1. have been awarded a second-cycle/advanced/master qualification (i.e. master degree), or
2. have satisfied the requirements for courses comprising at least 240 credits of which at least 60 credits were awarded in the advanced/second-cycle/master level, or
3. have acquired substantially equivalent knowledge in some other way in Sweden or abroad.*

Follow the instructions on the web page Entry requirements (eligibility) for doctoral education.

*If you claim equivalent knowledge, follow the instructions on the web page Assessing equivalent knowledge for general eligibility for doctoral education.

B) Specific eligibility requirement

You meet the specific eligibility requirement for doctoral/third-cycle/PhD education if you:

- Show proficiency in English equivalent to the course English B/English 6 at Swedish upper secondary school.

Follow the instructions on the web page English language requirements for doctoral education.

Verification of your documents Karolinska Institutet checks the authenticity of your documents. Karolinska Institutet reserves the right to revoke admission if supporting documents are discovered to be fraudulent. Submission of false documents is a violation of Swedish law and is considered grounds for legal action.

(A) and (B) can only be certified by the documentation requirement for doctoral education.

Skills and personal qualities

Necessary:

• Experience with quantitative data analysis and 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
• A strong personal interest to improve cancer care
• Very good communication skills in both writing and speaking, in English

Desirable/advantageous – Track 1

Experience with medical imaging data, particularly mammography or breast MRI Familiarity with risk prediction, survival analysis, or decision modelling Experience or strong interest in genetics, polygenic risk scores, or hereditary cancer Experience with training or fine-tuning machine learning or deep learning models Interest in clinical translation and personalized surveillance strategies

Desirable/advantageous – Track 2

Background in epidemiology, public health, biostatistics, or related quantitative fields Experience with population-based or registry data Familiarity with subgroup analysis, bias, or fairness assessment in statistical or ML models Experience with machine learning model training or fine-tuning is a strong merit Interest in health equity, responsible AI, and policy-relevant research 

Terms and conditions

The doctoral student will be employed on a doctoral studentship maximum 4 years full-time.

Application process

Submit your application and supporting documents through the Varbi recruitment system. Use the button in the top right corner and follow the instructions. We prefer that your application is written in English, but you can also apply in Swedish.

Your application must contain the following documents:

- A personal letter and a curriculum vitae
- Degree projects and previous publications, if any
- Any other documentation showing the desirable skills and personal qualities described above
- Documents certifying your general eligibility (see A above)
- Documents certifying your specific eligibility (see B above)

Selection

A selection will be made among eligible applicants on the basis of the ability to benefit from doctoral education. The qualifications of the applicants will be evaluated on an overall basis.

Karolinska Institutet uses the following bases of assessment:

- Documented subject knowledge of relevance to the area of research
- Analytical skill
- Other documented knowledge or experience that may be relevant to doctoral studies in the subject.

All applicants will be informed when the recruitment is completed.

Apply at the latest 14^th January.

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