PhD student in Machine Learning with a focus on mathematical foundations of uncertainty quantification in deep learning Full-time

PhD Student in Machine Learning with a Focus on Mathematical Foundations of Uncertainty Quantification in Deep Learning

Are you interested in developing mathematically grounded methods for uncertainty quantification in deep learning, particularly for large language models in healthcare applications? 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 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.

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 Large language models (LLMs) enable the extraction of clinical information from unstructured medical text. However, current LLM-based approaches often lack principled uncertainty quantification, limiting their reliability in healthcare applications. This project aims to develop mathematically grounded methods based on probability theory, statistical inference, and probabilistic modelling for uncertainty quantification in deep learning, particularly large language models. The focus will be on quantifying and evaluating uncertainty in predictions derived from medical reports, and on integrating these uncertainties into downstream probabilistic time-to-event models. Applications will focus on prostate cancer, using large-scale clinical registry data and unstructured medical text.

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 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:

• interest in mathematical and statistical method development,
• interest in uncertainty-aware machine learning,
• good communication skills and sufficient proficiency in oral and written English,
• creativity, thoroughness, and a structured approach to problem-solving.

Strong foundations in linear algebra, probability theory, and calculus are required. Solid skills in programming are required.

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

• Bayesian statistics,
• mathematical modelling,
• probabilistic machine learning,
• deep learning,
• large language models.

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 1 October 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 31 July 2026, UFV-PA 2026/1935.