PhD student in Machine Learning with a focus on mathematical and statistical methods for uncertainty quantification Full-time NEW

PhD Student in Machine Learning with a Focus on Mathematical and Statistical Methods for Uncertainty Quantification

Are you interested in probability theory, statistics, and mathematical modelling? Would you like to develop new methods for uncertainty quantification and apply these methods to large-scale clinical cancer data? Are you looking for an employer that invests in sustainable employeeship and offers safe, favorable working conditions? We welcome you to apply for a PhD position at the Department of Information Technology, Uppsala University.

The Department of Information Technology holds a leading position in both research and education at all levels. We are currently Uppsala University's third largest department, have around 350 employees, including 120 teachers and 120 PhD students. Approximately 5,000 undergraduate students take one or more courses at the department each year. You can find more information about us on the Department of Information Technology website.

At the Division of Systems and Control, we develop both theory and concrete tools to design systems that learn, reason, and act in the real world based on a seamless combination of data, mathematical models, and algorithms. Our research integrates expertise from machine learning, optimization, control theory, and applied mathematics, spanning diverse application domains such as medicine, energy systems, biomedical systems, neuroscience, and safety and security. The Division of Systems and Control enjoys a wide network of strong international collaborators all around the world, for example at the University of Oxford, the University of Melbourne, and the University of California, Los Angeles. We strive for all PhD students to get a solid international experience during their PhD.

The PhD position is part of the national research programme DDLS.

Data-driven life science (DDLS) uses data, computational methods and artificial intelligence to study biological systems and processes at all levels, from molecular structures and cellular processes to human health and global ecosystems. The SciLifeLab and Wallenberg National Program for Data-Driven Life Science (DDLS) aims to recruit and train the next generation of data-driven life scientists and to create globally leading computational and data science capabilities in Sweden. The program is funded with a total of 3.3 billion SEK over 12 years from the Knut and Alice Wallenberg (KAW) Foundation.

In 2026 the DDLS Research School will be expanded with the recruitment of 25 academic and 7 industrial PhD students. During the course of the DDLS program more than 260 PhD students and 200 postdocs will be part of the Research School. The DDLS program has four strategic research areas: cell and molecular biology, evolution and biodiversity, precision medicine and diagnostics, epidemiology and biology of infection. For more information, please see: https://www.scilifelab.se/data-driven/ddls-research-school/

The future of life science is data-driven. Will you be part of that change? Then join us in this unique program!

At Uppsala University, we are announcing the position as DDLS PhD student in data-driven precision medicine and diagnostics.

Data-driven precision medicine and diagnostics covers data integration, analysis, visualization, and data interpretation for patient stratification, discovery of biomarkers for disease risks, diagnosis, drug response and monitoring of health. The precision medicine research is expected to make use of existing strong assets in Sweden and abroad, such as molecular data (e.g. omics), imaging, electronic health care records, longitudinal patient and population registries and biobanks.

Project description Data-driven mathematical and statistical models are increasingly used in life science research and healthcare. Quantifying the uncertainty associated with these models is crucial for understanding their reliability and for making informed decisions based on their predictions. This project aims to develop new methods for uncertainty quantification in mathematical and statistical models. The methods will be applied to large-scale clinical cancer data. A central application will be quantifying uncertainty in information extracted from medical reports and propagating this uncertainty into downstream probabilistic time-to-event models.

Duties The doctoral student will primarily devote their time to graduate education. Other departmental duties of at most 20%, including teaching and administration, may also be included in the employment.

Requirements To meet the general entry requirements for doctoral studies, you must:

• hold a Master's (second-cycle) degree in applied mathematics, applied statistics, engineering physics, physics, machine learning, or in a similar subject, or
• have completed at least 240 credits in higher education, with at least 60 credits at Master's level including an independent project worth at least 15 credits, or
• have acquired substantially equivalent knowledge in some other way.

The University may permit an exemption from the general entry requirements for an individual applicant, if there are special grounds (Chapter 7, § 39 of the Higher Education Ordinance). For special entry requirements, please see the subject's general study plan.

We are looking for candidates with:

• strong foundations in linear algebra, probability theory, and calculus
• solid skills in programming
• interest in mathematical and statistical method development,
• good communication skills and sufficient proficiency in oral and written English,
• creativity, thoroughness, and a structured approach to problem-solving

Additional qualifications Experience with one or more of the following areas is meriting:

• Bayesian statistics,
• mathematical modelling,
• statistical machine learning.

Application The application must include:

1. a one-page cover letter outlining how you meet the required criteria, your motivation for applying to this position, and your expected earliest starting date;
2. a CV;
3. degrees and transcript of records with grades (translated to English or Swedish);
4. a Master's thesis (or a draft thereof, and/or some other self-produced technical or scientific text), publications, and other relevant documents;
5. references with contact information (names, emails and telephone number), and up to two reference letters.

About the employment The employment is a temporary position according to the Higher Education Ordinance chapter 5 § 7. Scope of employment 100 %. Starting date 15 November 2026 or as otherwise agreed. Placement: Uppsala.

For further information about the position, please contact: Assistant Professor Sara Hamis, e-mail: sara.hamis@it.uu.se.

Please submit your application by 16 October 2026, UFV-PA 2026/2836.

Uppsala University is a broad research university with a strong international position. The ultimate goal is to conduct education and research of the highest quality and relevance to make a difference in society. Our most important asset is all of our 7,500 employees and 53,000 students who, with curiosity and commitment, make Uppsala University one of Sweden's most exciting workplaces.

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. If security vetting is conducted, the applicant must pass the vetting process to be eligible for employment.

Please do not send offers of recruitment or advertising services.

Submit your application through Uppsala University's recruitment system.

Employee organizations: Saco-S – saco-s@uu.se, Seko – seko@uadm.uu.se, ST (OFR/S) – ofr@uu.se

Type of employment – Temporary position

Contract type – Full time

First day of employment – 2026-11-15 or as agreed

Salary – Fixed salary

Number of positions – 1

Full-time equivalent – 100

City – Uppsala

County – Uppsala län

Country – Sweden

Reference number – UFV-PA 2026/2836

Published – 23.Sep.2026

Last application date – 16.Oct.2026