PhD student in Computerised Image Processing with focus on Machine Learning for Data-Driven Precision Medicine and Diagnostics Full-time NEW

PhD Student in Computerised Image Processing with Focus on Machine Learning for Data-Driven Precision Medicine and Diagnostics

Are you interested in developing new image analysis and machine learning methods for cancer diagnostics and clinical decision support? Would you like to work in a multidisciplinary team together with competent and friendly colleagues in an international environment? 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, with 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.

The project is led by Professor Joakim Lindblad, at the Centre for Image Analysis and the Vi3 division, Department of Information Technology, and is carried out together with researchers who develop methods in deep learning and image analysis with applications in life sciences and medicine. The project is based on a close collaboration with researchers at the Department of Immunology, Genetics and Pathology (IGP) at Uppsala University and SciLifeLab.

About the DDLS research program 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, visualisation, 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 The development of artificial intelligence (AI) and computerised image processing in combination with advanced digital microscopy is enabling major advances in clinical pathology and cancer diagnostics. Today's AI methods require large amounts of data with a detailed ground truth annotation that the AI system can learn from. In healthcare, in most cases, there is only access to information at the patient level, about the patient's health status and disease development. In this project, we will develop theory, algorithms and methods to effectively train AI models based on limited and imprecise information as well as unbalanced and heterogeneous multimodal data.

This needs-driven method development finds direct application in healthcare. Together with our partners in healthcare and biomedicine, we will apply the developed methods to detect cancer from cell and tissue samples as early as possible and to individually predict which treatment is expected to give the best result for the patient.

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 computer science, image analysis and machine learning, engineering physics, molecular biotechnology engineering, data sciences, applied mathematics, or another related field, 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 specific requirements are met by having passed exams in areas relevant to the subjects of image analysis and machine learning with a minimum of 90 higher education credits (ECTS). Relevant courses include, for example, image processing, computer vision, machine learning, deep learning and neural networks, as well as courses in python, GPU programming, mathematical modeling and statistics, or equivalent.

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:

• A solid academic background with thorough computational and analytical understanding;
• Proficiency in programming in Python and deep learning frameworks such as PyTorch and TensorFlow;
• Excellent communication skills in oral and written English;
• Creativity, thoroughness, and a structured approach to problem-solving;
• Good collaborative skills, drive, and independence.

Additional qualifications Meriting are:

• Interest in biomedical research and experience in application of image analysis in medicine.
• Experience of programming in JavaScript, software version control with Git, typesetting with LaTeX, use of Linux computers;
• Experience with convolution and transformer based neural networks for image analysis.

Application The application should consist of:

• A Curriculum Vitae (CV);
• A copy of a degree/diploma, and transcript of records with grades (translated into English or Swedish);
• Master's thesis (or a draft thereof) and/or some other self-produced technical or scientific text, scientific publications, and other relevant documents, in electronic form;
• Contact details (names, emails, and telephone numbers) of minimum two references, also specifying the context, duration, and nature of the relationship with the candidate. Reference letters may be provided as supporting documents but are not required at the time of the application.
• A personal letter (max 1 page) which includes:
• Described motivation for the application for this position;
• The earliest possible starting date of employment;
• Up to three main scientific achievements.

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 September 2026 or as agreed. Placement: Uppsala

For further information about the position, please contact: Prof. Joakim Lindblad (e-mail: joakim.lindblad@it.uu.se)

Please submit your application by 22 May 2026, UFV-PA 2026/1318.