项目介绍

2026年6月5日

Do you want to contribute to improving human health?

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 Translational embryonal neural tumor research laboratory is located at BioClinicum in Stockholm and affiliated with the Division of Pediatric Oncology and Pediatric Surgery, Dept of Women’s and Children’s Health, Karolinska Institutet. The Baryawno laboratory consists of 2 Ph.D. students and a postdoc, and is part of a group constellation consisting of professor Per Kogner, associate professor John Inge Johnsen and associate professor Malin Wickström. The group consists of a total of 12 Ph.D. students and postdoctoral fellows. The focus of our research is to increase our biological understanding of the embryonal tumor neuroblastoma, to develop new therapies leading to better survival, less side effects and increased quality of life. We are applying basic, translational and clinical approaches in our research including, single cell technologies, bioinformatics, immunology and molecular biology as well as functional analysis and development of preclinical animal- and in vitro-models. Over the past year the group has invested heavily in computational and AI‑driven research, and the present position is part of that strategic direction.

The doctoral student project and the duties of the doctoral student

The PhD project will develop and apply state‑of‑the‑art machine‑learning and deep‑learning frameworks that integrate molecular, histopathological and clinical data to address two of the most pressing problems in cancer care: therapy resistance and metastasis. The thesis is organised in three complementary sub‑studies:

• Pan‑cancer therapy resistance. Representation learning across large pharmacogenomic, transcriptomic and clinical datasets to identify recurrent programmes of treatment failure in adult cancers, and to nominate intervention points amenable to therapeutic targeting.
• Pan‑cancer metastasis. Contribution to multimodal AI models that predict metastatic risk and trajectory from molecular and clinical features across cancer types, building on ongoing pipelines in the group.
• Pan‑pediatric oncology foundation model. Pre‑training a foundation model on bulk whole‑genome sequencing, whole‑transcriptome, methylation and digital histopathology data across pediatric solid tumours, and fine‑tuning it for therapy‑response prediction.

The doctoral candidate will be primarily supervised by Dr Ninib Baryawno and Francisco J. Peña, machine learning expert (KTH Royal Institute of Technology). The doctoral candidate will be working closely with the rest of the group constellation and with external collaborators in machine learning, computational pathology and pediatric oncology. More information on the work from the lab can be found in recent publications (Baryawno et al., Cell 2019; Kfoury, Baryawno et al., Cancer Cell 2021; Verhoeven et al., Cell Reports Medicine 2022; Alchahin et al., Nature Communications 2022; Olsen et al., JCI Insight 2022; Olsen et al., Molecular Cancer 2024).

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 requirementYou 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

We are looking for a highly motivated candidate with a strong quantitative profile and a genuine interest in cancer research. The PhD candidate should hold a master’s degree (or equivalent credits at advanced level e.g., a minimum of at least 240 ECTS credits) in data science, computer science, bioinformatics, computational biology, biomedical engineering or a related quantitative discipline.

Essential:

• Strong programming skills in Python and demonstrated experience working in a reproducible way (Git, version control, scripted pipelines, clear documentation).
• Solid foundation in machine learning and hands‑on experience with at least one modern deep‑learning framework (PyTorch, TensorFlow or JAX).
• Comfort handling large, heterogeneous and real‑world datasets, including missingness, confounders, batch effects and class imbalance.
• Ability to work independently, communicate clearly in English, and collaborate effectively across disciplines.

Meritorious:

• Experience with representation learning (contrastive learning, autoencoders, masked modelling), multimodal models or foundation models for biomedical data.
• Previous research exposure to oncology, biomedicine or clinical data; familiarity with high‑performance / GPU computing environments.

The ability to work with people of different backgrounds and contribute constructively to an interdisciplinary project team is essential. Prior wet‑lab experience is not required; the position is firmly computational.

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.

KI applies a salary scale when setting salaries for doctoral students. All applicants will be informed when the recruitment is completed.

Want to make a difference? Join us and contribute to better health for all