PhD student in Computerized Image Processing with focus on Applications in data-driven precision medicine

Are you interested in developing new image analysis and machine learning methods for precision medicine and clinical decision support Would you like to work together with competent and inspiring colleagues in an international environment Are you looking for an employer that invests in sustainable employeeship and offers safe favourable 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 Uppsala Universitys third largest department with 350 employees including 120 teachers and 120 PhD students. Approximately 5000 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 will be led by Professor Nataša Sladoje within the MIDA Methods for Image Data Analysis research group at the Department of Information Technology and will be conducted alongside other researchers at the Centre for Image Analysis who develop computational methods with a particular focus on deep learning and image analysis. The project relies on a close collaboration with researchers at the Department of Immunology Genetics and Pathology (IGP) at Uppsala University and SciLifeLab the national infrastructure for life science.

Project description
Immunotherapy has become a life-saving option for advanced cancer patients. However only a minority of patients develop a durable response. Many researchers are investing efforts to understand the complexity of anti-cancer immunity and develop diagnostic approaches that accurately predict therapy benefit and enable successful individualized cancer therapy planning. Contemporary AI-based approaches show great promise to advance this research frontier.

This project is a key part of our broader initiative to develop and utilize innovative interpretable data-driven analysis methods to significantly advance our understanding of immune cell inter-relations within the cancer microenvironment. We will apply these analysis methods to highly informative multimodal microscopy data and develop techniques to integrate correlated structural and molecular analysis into the natural 3D tissue space. This integration will advance the ability to predict disease progression and response to specific therapies.

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 Masters degree in computer science image analysis and machine learning engineering physics data science applied mathematics molecular biotechnology engineering or another related field; or
• Have completed at least 240 credits in higher education with at least 60 credits at Masters 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 subjects general study plan.

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.

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 software version control with Git typesetting with LaTeX use of Linux computers;
• Experience with convolution and transformer-based neural networks for image analysis;
• Experience with graph-based methods and graph convolutional/neural networks;
• Experience with explainable and interpretable AI (XAI).

Rules governing PhD students are set out in the Higher Education Ordinance chapter 5 1-7 and in Uppsala Universitys rules and guidelines.

Application
The application should consist of:

1. A Curriculum Vitae (CV);
2. A copy of a degree/diploma and transcript of records with grades (translated into English or Swedish);
3. Masters thesis (or a draft thereof) and/or some other self-produced technical or scientific text scientific publications and other relevant documents in electronic form;
4. 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 document but are not required at the time of the application.
5. A personal letter (max 1 page) which includes
   1. Described motivation for the application for this position;
   2. Up to three main scientific achievements;
   3. The earliest possible starting date of employment.

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 agreed. Placement: Uppsala

For further information about the position please contact: Prof. Nataša Sladoje (email: ).

Please submit your application by 20 August 2026 UFV-PA 2026/2366.

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