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

PhD Position 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, favourable working conditions? We welcome you to apply for a PhD position at the Department of Information Technology, Uppsala University.

The project is led by Professor Joakim Lindblad, 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.

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.

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.

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 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 is:

• 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.

The application should consist of:

1. A Curriculum Vitae (CV);
2. 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 documents 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. The earliest possible starting date of employment;
3. Up to three main scientific achievements.

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 1 June 2026, UFV-PA 2026/1408.