Doctoral (PhD) student position in deep learning modeling of cancer

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

This position is based in Avlant Nilsson’s research group at Karolinska Institutet and SciLifeLab. We develop mechanistic AI models of cancer cells to advance precision medicine. By integrating large-scale multi-omics data (e.g. metabolomics, transcriptomics, proteomics) with prior knowledge of molecular interaction networks, we construct interpretable deep learning models that capture system-level mechanisms in cancer.

The central goal of the lab is to build a unified computational model that describes how different molecular processes (signaling, gene regulation, and metabolism) interact to determine cellular behavior. The purpose is to identify biomarkers, novel drug targets, and mechanisms of resistance, and ultimately to enable computer-aided design of precision therapies.

The project is part of the SciLifeLab and DDLS (Data-Driven Life Science) program, providing access to a highly collaborative and interdisciplinary research environment and state-of-the-art computational infrastructure, including the Berzelius supercomputer.

The main supervisor is Assistant Professor Avlant Nilsson and Professor Randall S. Johnson at the Department of Cell and Molecular Biology will be co-supervisor.

The doctoral student project and the duties of the doctoral student

Cancer emerges from the accumulation of genetic and other alterations that reshape interconnected cellular networks and give rise to aberrant phenotypes. Cellular metabolism is highly dynamic in cancer and dysregulation of metabolism is considered a hallmark of tumorigenesis. While metabolism, signaling, and gene regulation are tightly interconnected, they have traditionally been studied using separate modeling frameworks. This limits our ability to understand how they interact, e.g. predict the metabolic effects of drugs that target cellular signaling.

In this PhD project, you will develop a unified, next-generation AI model of the cancer cell by integrating metabolism, signaling, and gene regulation into a single predictive framework. The work will involve designing biologically constrained deep learning models, using a novel architecture based on recurrent neural network (RNN)-based approaches, to capture how molecular states evolve and give rise to cellular phenotypes.

The project includes three main components:

• Model development: integrating modules of cellular subsystems to predict cellular states
• In silico target discovery: predicting metabolic vulnerabilities and prioritizing candidates for experimental validation
• Biomarker discovery: identifying molecular states associated with drug response across cancer contexts

You will work closely with our computational team and collaborators, and contribute to designing experiments guided by model predictions.

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 Skills:

• Strong programming skills in languages such as Python.
• Familiarity with version control (e.g. Git)
• The ability to work both independently and collaboratively within a team.
• Proficiency in written and spoken English.

Desirable qualifications:

• Background in physics, computational biology, machine learning, applied mathematics, or similar
• Experience with deep learning frameworks (e.g. PyTorch, TensorFlow)
• Familiar with cancer-, and cell biology.
• Familiarity with high-dimensional or dynamical systems
• Experience analyzing biological or multi-omics data

Prior biological expertise is not required—we particularly encourage applicants from quantitative disciplines.

Our group values diversity and inclusion, and we are committed to fostering an environment where all individuals feel empowered to contribute their unique perspectives and skills towards our shared goal of advancing precision cancer medicine.

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.

Your application should contain the following documents:

• A personal letter and a curriculum vitae (Swedish or English)
• Degree projects and previous publications, if any (Swedish or English)
• Any other documentation showing the desirable skills and personal qualities described above (Swedish or English)
• 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.

Job details

Type of employment: PhD placement
Contract type: Full time
First day of employment: According to agreement
Salary: Monthly salary
Full-time equivalent: 100
City: Stockholm
County: Stockholms län
Country: Sweden
Reference number: STÖD 2-1891/2026
Contact:

1. Avlant Nilsson, avlant.nilsson@ki.se

Union representative:

1. Björn Andersson, SACO, bjorn.andersson@ki.se
2. Elizabeth Valenzuela, SEKO, elizabeth.valenzuela@ki.se
3. Bodil Moberg, OFR, bodil.moberg@ki.se

Published: 2026-05-07
Last application date: 2026-05-27