Vacant job

PhD Student in AI and Machine Learning for Cancer Research

The Department of Immunology, Genetics and Pathology at Uppsala University has a broad research profile with strong research groups focused on, among other things, cancer, autoimmune and genetic diseases. A core principle of the department is to stimulate translational research and thereby foster closer collaboration between medical research and healthcare. Read more about the department here: https://www.uu.se/institution/immunologi-genetik-och-patologi (https://www.uu.se/institution/immunologi-genetik-och-patologi)

Do you want to use artificial intelligence and machine learning to understand — and reprogram — the behavior of one of the world's deadliest cancers? We are looking for a PhD student who wishes to conduct world-class research on glioblastoma: a brain tumor whose mortality is not driven by metastasis to other organs, but by its ability to continuously change its biological character and invade brain tissue. With us, you will have the opportunity to combine cutting-edge AI methodology development with direct experimental validation, in an international research environment with strong collaborations and unique resources.

The research group is led by Professor Sven Nelander at IGP Uppsala University. We work at the intersection of AI/machine learning, systems biology, and experimental neuro-oncology. Over the past decade, our group has built unique resources: a biobank with over 100 patient-derived glioblastoma cell lines (distributed to 54 laboratories in 17 countries), large-scale Perturb-seq data, in-house developed CRISPR reporter tools, and a prototype system for multimodal AI. The group is part of the national strategic research center CNSx3 and is connected to UUniFi's AI Institute. A unique context for cutting-edge research with genuine interdisciplinary characteristics. The project is funded by the Swedish Research Council, the Swedish Cancer Society, KAW, and SSF.

Job Responsibilities

The fundamental problem we aim to solve is understanding how cells in a brain tumor choose to change their character — and how we can use this knowledge to actively steer them toward states that are more sensitive to treatment. We call this state steering. Unlike conventional cancer therapy, which aims to kill tumor cells with broadly acting therapies, state steering aims to reprogram them. Through reprogramming, a range of effects can be achieved, such as reduced invasion, increased sensitivity to radiation, or senescence.

You will work with two main types of data collected within the framework of the strategic research center CNS×3: large-scale intervention experiments, where we systematically map how genetic and pharmacological perturbations affect tumor cell plasticity, and image-based tracking data, where individual tumor cells are followed in real-time as they migrate through brain tissue. The goal is to build AI models that integrate these data sources and can predict how a given treatment affects the tumor's behavior and outcome.

Existing tools such as Hidden Markov Models (HMM), used to analyze the movement of individual cells in image data, are special cases of this framework. The new contribution, rewire-seq, is a more general and data-scalable version that combines large-scale intervention experiments with image-based cell tracking to provide a coherent picture of tumor dynamics. A key component of the project is cyclic experimentation: the model's predictions directly guide the choice of the next experiment, and the results are fed back into the model — a process that makes the research progressively more precise. You will work closely with experimental colleagues in a team-based environment, jointly translating biological questions into computational solutions with clinical relevance.

Qualifications

Eligibility for doctoral studies requires that the applicant has

• completed a master's degree (or equivalent) in computational biology, bioinformatics, machine learning, applied mathematics, biophysics, molecular biology, or similar, or
• completed at least 240 higher education credits, including at least 60 credits at advanced level, including an independent project of at least 15 credits, or
• acquired substantially equivalent knowledge in some other way.

The project requires a solid understanding of the mathematics underlying AI and machine learning — for example, linear algebra, probability theory, and statistical inference. You should have practical experience with demanding data analyses, e.g., in genomics, image analysis, or biochemical screening data. Strong programming skills in Python, R, or another language are required. We are open to applicants with the potential to combine experimental and computational work. The project suits you if you wish to work with cutting-edge research in academia or industry in the long term. Personal suitability is highly valued. Excellent spoken and written English is required for the role.

Desirable/Additional Qualifications

Experience with analysis of single-cell RNA sequencing data, CRISPR screening, or image data. Experience with deep learning or graph neural networks. Experience with modeling dynamic systems or system control. Previous experience in methodology development or published research. Interest in cancer biology and the ability to formulate biological hypotheses based on data.

Regulations for doctoral students can be found in the Higher Education Ordinance Chapter 5, Sections 1-7, as well as in the university's rules and guidelines.

About the Position

The employment is fixed-term, according to the Higher Education Ordinance Chapter 5, Section 7. The position is full-time. Start date: as soon as possible or by agreement. Location: Uppsala

Information about the position can be obtained from: Sven Nelander, [email protected].

Please submit your application no later than August 16, 2026, UFV-PA 2026/1605.

Read more about our benefits and what it is like to work at Uppsala University: https://uu.se/om-uu/jobba-hos-oss/ (https://uu.se/om-uu/jobba-hos-oss/)

The position may be subject to a security clearance. If a security clearance is required, approval is a prerequisite for employment.

We kindly decline offers of assistance with recruitment and advertising.

Applications are received through Uppsala University's recruitment system.

Union Representatives: Saco-S - [email protected], Seko - [email protected], ST (OFR/S) - [email protected]