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

This doctoral (PhD) student position at Karolinska Institutet offers an exciting opportunity to contribute to advancing human health through deep learning modeling of cancer. The position is based in Avlant Nilsson’s research group at Karolinska Institutet and SciLifeLab, focusing on developing mechanistic AI models of cancer cells to support precision medicine. The group integrates large-scale multi-omics data—including metabolomics, transcriptomics, and proteomics—with molecular interaction networks to 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 describing how molecular processes such as signaling, gene regulation, and metabolism interact to determine cellular behavior. This research aims to identify biomarkers, novel drug targets, and mechanisms of resistance, ultimately enabling 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, with Professor Randall S. Johnson as co-supervisor at the Department of Cell and Molecular Biology. The PhD project involves developing a unified, next-generation AI model of the cancer cell by integrating metabolism, signaling, and gene regulation into a single predictive framework. The work includes designing biologically constrained deep learning models using novel architectures based on recurrent neural networks (RNNs) to capture how molecular states evolve and give rise to cellular phenotypes.

Project components include: model development (integrating modules of cellular subsystems to predict cellular states), in silico target discovery (predicting metabolic vulnerabilities and prioritizing candidates for experimental validation), and biomarker discovery (identifying molecular states associated with drug response across cancer contexts). The student will collaborate closely with the computational team and contribute to experiment design guided by model predictions.

Karolinska Institutet is one of the world's leading medical universities, offering a creative and inspiring environment full of expertise and curiosity. The university conducts successful medical research and holds the largest range of medical education in Sweden. As a doctoral student, you will be offered an individual research project, a well-educated supervisor, a wide range of elective courses, and the opportunity to work in a leading research group. International exchanges are possible through collaborations with prominent universities worldwide.

The position is a full-time doctoral studentship for up to four years, with a contractual monthly salary. Employees have access to a modern gym for free and receive reimbursements for medical care. The eligibility requirements include a master’s degree or equivalent, proficiency in English, and strong programming skills in Python. Desirable qualifications include backgrounds in physics, computational biology, machine learning, applied mathematics, experience with deep learning frameworks, and familiarity with cancer and cell biology. Prior biological expertise is not required, and applicants from quantitative disciplines are encouraged.

Applications must be submitted through the Varbi recruitment system by 2026-05-27. Required documents include a personal letter, CV, degree projects and previous publications, documentation of skills and personal qualities, and documents certifying eligibility. Selection is based on subject knowledge, analytical skill, and other relevant experience. All applicants will be informed when recruitment is completed.

For further information, contact Assistant Professor Avlant Nilsson ([email protected]). Join Karolinska Institutet and contribute to better health for all!