Doctoral student in AI and clinical data science for breast cancer Full-time NEW

Doctoral Student In Ai And Clinical Data Science For Breast Cancer

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 doctoral student will be based at the Department of Oncology-Pathology, Karolinska Institutet, within the Computational Breast Imaging Group led by Associate Professor Fredrik Strand. The group is internationally recognized for the development and evaluation of AI for breast cancer imaging and precision medicine, combining expertise in radiology, machine learning, and biostatistics. It comprises PhD students, research engineers, research assistants, postdoctoral researchers, and collaborating senior clinicians in radiology, pathology, oncology, and surgery. The group has active national collaborations (KTH, Lund University, the Institute for Future Studies, and several groups at KI) and international collaborations (among others UC Berkeley, UCSF, University of Hawaii, and University of Barcelona). Available infrastructure includes high-performance computing, GDPR-compliant secure storage, the VAI.B platform, and a large linked retrospective breast cancer cohort with imaging, clinical documentation, and registry data. The doctoral student will be supervised by Apostolia Tsirikoglou, PhD, Lead AI Scientist in the group, as main supervisor, together with Associate Professor Fredrik Strand, head of the group and principal investigator of the project, as co-supervisor.

This PhD project develops agent-based data infrastructure and methods for transforming longitudinal, multimodal data into structured clinical decision-state representations and benchmark datasets for correctness-oriented evaluation of AI-supported multidisciplinary decision-making. Breast cancer care involves complex, longitudinal decisions across radiology, pathology, surgery, oncology, and multidisciplinary tumor boards, based on evolving information distributed across imaging, reports, clinical notes, and registries. Current AI systems are typically built for isolated tasks and rarely capture the temporal and multidisciplinary structure of real clinical decision-making. The work combines data engineering, machine learning, and language technologies, and uses a large retrospective breast cancer cohort (more than 30,000 cases) linked to the National Quality Register for Breast Cancer. The project is conducted in close collaboration with clinical specialists. The doctoral student is expected to take an active and increasingly independent role in formulating research questions, developing methods, working with clinical data, collaborating with clinicians and computer scientists, writing scientific publications, and presenting results at international conferences.

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.

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.

Necessary:

• A master's degree (or equivalent) in image analysis, machine learning, computer science, biomedical engineering, data science, or a related field
• Solid programming skills, in particular Python
• Experience with machine learning and/or image analysis
• Experience with data engineering, data pipelines, or working with heterogeneous data sources
• Strong analytical ability and interest in interdisciplinary research at the intersection of AI and clinical medicine
• Ability to work both independently and collaboratively in a multidisciplinary team
• Excellent written and spoken English
• Desirable/advantageous:
• Experience with health data standards (e.g. FHIR, OMOP, DICOM) or clinical data
• Experience with multimodal data integration or temporal/longitudinal data
• Familiarity with containerization and reproducible research workflows (e.g. Docker)
• Interest in trustworthy AI, evaluation methodology, and clinical translation

The doctoral student will be employed on a doctoral studentship for a maximum of 4 years full-time. The position can also be held part-time, at a minimum of 50%.

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)

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.

KI applies a salary scale when setting salaries for doctoral students.

Apply at the latest 20th October.