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

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 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 Master's 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 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.

  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.

    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).

      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. 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;
      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: natasa.sladoje@it.uu.se).

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