About the position
Hospitals and researchers increasingly rely on mathematical and statistical models built from data, and they need to know how far to trust each prediction. This PhD develops new ways of measuring that uncertainty and applies them to large-scale clinical cancer data.
A central task is to measure how uncertain the information pulled out of medical reports is, and to carry that uncertainty through into time-to-event models, which estimate how likely an event is to happen by a given time. The post is in the Division of Systems and Control at Uppsala University's Department of Information Technology, and is part of DDLS, Sweden's national programme in data-driven life science, which adds 25 academic PhD students to its research school in 2026.
What you will do
- Develop new mathematical and statistical methods for uncertainty quantification.
- Apply them to real clinical cancer data as part of the DDLS research school in precision medicine and diagnostics.
- Spend up to 20% of your time teaching or on department work.
Who can apply
- A master's degree in applied mathematics, applied statistics, engineering physics, physics, machine learning or a similar subject, or at least 240 credits of university study including 60 at master's level with an independent project of at least 15 credits, or equivalent knowledge.
- Strong foundations in linear algebra, probability and calculus, and solid programming skills.
- Good spoken and written English.
- An advantage: Bayesian statistics, mathematical modelling or statistical machine learning.
What is offered
- Full-time, fixed-term employment as a doctoral student, on a fixed monthly salary.
- Start on 15 November 2026, or as agreed.
- The post may involve a security check before you are hired.
How to apply
- Apply through Uppsala University's recruitment system, linked below, quoting reference UFV-PA 2026/2836.
- A one-page cover letter on how you meet the requirements, why you want the post, and your earliest start date.
- A CV, plus your degree certificates and transcripts with grades, translated into English or Swedish.
- Your master's thesis or a draft of it (or another technical text you wrote), and any publications.
- Contact details for your referees, and up to two reference letters.