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Are you interested in developing mathematically grounded methods for uncertainty quantification in deep learning, with a special focus on large language models for applications within healthcare? Do you want an employer that invests in sustainable employee relations and offers secure, advantageous working conditions? You are welcome to apply for a doctoral position at Uppsala University.

The Department of Information Technology holds a leading position in both research and education at all levels. Today, we are Uppsala University's third largest department, with over 350 employees, including 120 faculty members and 120 doctoral students. Approximately 5,000 undergraduate students take one or more courses at the department each year. More information about us can be found on the Department of Information Technology's website.

About the DDLS Research Programme

The doctoral 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. SciLifeLab and the Wallenberg National Program for Data-Driven Life Science (DDLS) aim to recruit and train the next generation of data-driven life science researchers and to create globally leading competence in computational and data science in Sweden. The programme is funded with a total of 3.3 billion SEK over 12 years by the Knut and Alice Wallenberg Foundation (KAW).

In 2026, the DDLS doctoral school will be expanded through the recruitment of 25 academic and 7 industry doctoral students. During the programme, more than 260 doctoral students and 200 postdocs will be part of the doctoral school. The DDLS programme has four strategic research areas: cell and molecular biology, evolution and biodiversity, precision medicine and diagnostics, and epidemiology and infection biology. For more information, see: https://www.scilifelab.se/data-driven/ddls-research-school/ (https://www.scilifelab.se/data-driven/ddls-research-school/)

The future of life sciences is data-driven. Do you want to be part of this change? Then you are welcome to participate in this unique programme!

Project Description

Large Language Models (LLMs) enable the extraction of clinical information from unstructured medical text. However, current LLM-based methods often lack principled uncertainty quantification, which limits their reliability in healthcare applications. The project aims to develop mathematically grounded methods for uncertainty quantification in deep learning, with a special focus on large language models, where the methods are based on probability theory, statistical inference, and probabilistic modelling. The focus is on quantifying and evaluating uncertainty in predictions derived from medical records, as well as integrating these uncertainties into subsequent probabilistic time-to-event models. The applications will focus on prostate cancer and use large-scale clinical register data as well as unstructured medical text.

Responsibilities

The doctoral student shall primarily devote themselves to their own doctoral studies. Other duties at the department, such as teaching and administrative work, may be included within the scope of employment (max 20%).

Qualification Requirements

Eligibility for graduate studies is granted to those who have:

• completed an advanced level degree in applied mathematics, applied statistics, technical physics, physics, machine learning, or a similar field, or
• completed at least 240 higher education credits, of which at least 60 credits at advanced level, including an independent work of at least 15 higher education credits, or
• acquired in some other way substantially equivalent knowledge.

The university may grant exceptions to the requirement for basic eligibility for an individual applicant if there are special reasons. (Chapter 7, Section 39 of the Higher Education Ordinance). For specific eligibility, see the study plan for the subject.

We are looking for candidates with:

• an interest in method development in applied mathematics and statistics,
• an interest in uncertainty-aware machine learning,
• good communication skills and sufficient knowledge of English in speech and writing,
• creativity, precision, and a structured approach to problem-solving.

Strong knowledge in linear algebra, probability theory, and analysis is required. Good programming skills are also required.

Desirable/Additional Qualifications

Experience in one or more of the following areas is desirable:

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

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.

Application

The application should include:

• a personal letter (max 1 page) explaining how you meet the qualification requirements, motivating why you are applying for this position, and your estimated earliest start date;
• a curriculum vitae (CV);
• diplomas and transcripts with grades (translated into English or Swedish);
• thesis (or draft thereof, and/or other self-produced technical or scientific text), publications, and other relevant documents;
• references with contact information (name, email, and phone number) and up to two letters of recommendation.

About the Employment

The employment is fixed-term, according to HE Ordinance Chapter 5 Section 7. The position is full-time. Start date: October 1, 2026, or as agreed. Location: Uppsala.

Information about the position is provided by: Assistant Professor Sara Hamis, e-mail: [email protected].

Please submit your application no later than July 31, 2026, UFV-PA 2026/1935.