PhD Student in AI and Machine Learning for Cancer Research

Description:

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 cancer, autoimmune and genetic diseases, among other areas. One of the fundamental ideas at the department is to stimulate translational research and thereby closer collaboration between medical research and healthcare. Read more about the department here: 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 most dangerous cancers? We are looking for a PhD student who wants to conduct world-class research on glioblastoma: a brain tumor whose mortality is not driven by spread to other organs, but by its ability to continuously change biological character and invade brain tissue. With us, you get the opportunity to combine cutting-edge AI method 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 more than a decade, our group has built up unique resources: a biobank with over 100 patient-derived glioblastoma cells (distributed to 54 laboratories in 17 countries), large-scale Perturb-seq data, self-developed CRISPR reporter tools, and a prototype system for multimodal AI. The group is part of the national strategic research center CNSx3 and has links to UUniFI's AI institute. A unique setting for cutting-edge research with genuine interdisciplinary character. The project is funded by the Swedish Research Council, the Cancer Society, KAW and SSF. Responsibilities The fundamental problem we want to solve is to understand how cells in a brain tumor choose to change character — and how we can use that knowledge to actively direct them toward more treatment-sensitive states. We call this state steering. Unlike conventional cancer therapy, which aims to kill tumor cells with broad-acting therapies, state steering aims to reprogram them. Through reprogramming, a range of effects are achieved, such as reduced invasion, increased sensitivity to radiation, or senescence. You will work with two main types of data collected as part 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 monitored in real time as they migrate through brain tissue. The goal is to build AI models that combine these data sources and can predict how a given treatment affects tumor behavior and outcomes. Existing tools such as hidden Markov models (HMM), which are used to analyze individual cell movements 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 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 and together transform biological questions into computational solutions with clinical relevance. Qualification Requirements Eligible for education at the research level is a person who has - completed a degree at advanced level in computational biology, bioinformatics, machine learning, applied mathematics, biophysics, molecular biology, or similar, or - completed at least 240 credits, of which at least 60 credits at advanced level including an independent project of at least 15 credits, or - acquired in essence equivalent knowledge in some other way. The project requires solid understanding of the mathematics underlying AI and machine learning — for example linear algebra, probability theory and statistical inference. You should have concrete experience with demanding data analysis, for example in genomics, image analysis or biochemical screening data. Strong programming skills in Python, R or another language are necessary. We are open to applicants with the potential to combine experimental and computational work. The project suits you if in the long term you want to work with cutting-edge research in academia or industry. Great emphasis is placed on personal suitability. Excellent spoken and written English is required for the role. Desirable/Meriting Experience Experience with single-cell RNA sequencing analysis, CRISPR screening, or image data. Experience with deep learning or graph neural networks. Experience with dynamic systems modeling or systems control. Previous experience with method development or published research. Interest in cancer biology and ability to formulate biological hypotheses based on data. Provisions for PhD students are found in the Higher Education Ordinance Chapter 5 Sections 1-7 and in the university's rules and guidelines. About the Position The position is fixed-term, according to HF Chapter 5 Section 7. Full-time. Start as soon as possible or by agreement. Place of work: Uppsala Information about the position is provided by: Sven Nelander, [email protected]. We welcome your application by 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/ The position may be subject to security vetting. During security vetting, a condition for employment is that the applicant is approved. We decline offers of recruitment and advertising assistance. Applications are received in Uppsala University's recruitment system. Trade union representatives: Saco-S - [email protected], Seko - [email protected], ST (OFR/S) - [email protected]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 cancer, autoimmune and genetic diseases, among other areas. One of the fundamental ideas at the department is to stimulate translational research and thereby closer collaboration between medical research and healthcare. Read more about the department here: https://www.uu.se/institution/immunologi-genetik-och-patologi Do you want to use artificial intelligence and machine learning to understand…

Overview

Type

job

Status

active

Visibility

public

City

Uppsala

Address

box 256

GPS

59.8710738, 17.5946002copyopen in

Email

Show email

Views

11

Published

25. 6. 2026

Edited

5. 8. 2026

Location

Specifications