Uppsala University
2 months ago
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PhD in Computerized Image Processing for Data-Driven Precision Medicine Uppsala University in Sweden
Degree Level
PhD
Field of study
Computer Science
Funding
Temporary full-time PhD employment at 100% according to the Higher Education Ordinance. Salary is listed as fixed salary; no stipend amount is given. The position includes doctoral studies and may include up to 20% departmental duties such as teaching and administration.
Deadline
Expired
Country
Sweden
University
Uppsala University
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About this position
PhD position in Computerized Image Processing for Data-Driven Precision Medicine at Uppsala University, Sweden, within the Department of Information Technology and the MIDA (Methods for Image Data Analysis) research group.
The project is led by Professor Nataša Sladoje and is carried out in collaboration with the Centre for Image Analysis, the Department of Immunology, Genetics and Pathology (IGP), and SciLifeLab. The research focuses on developing new image analysis, machine learning, and deep learning methods for precision medicine, especially for interpreting multimodal microscopy data in cancer and immunotherapy research. The work aims to improve understanding of immune cell interactions in the cancer microenvironment and support disease progression and therapy-response prediction.
This is a 100% full-time temporary PhD employment. The position includes doctoral education and may include up to 20% departmental duties such as teaching and administration. The starting date is 1 October 2026 or as agreed. The workplace is Uppsala, Sweden.
Eligibility and requirements: applicants should have a Master’s degree or equivalent background in computer science, image analysis and machine learning, engineering physics, data science, applied mathematics, molecular biotechnology engineering, or a related field. A minimum of 90 ECTS in relevant areas such as image processing, computer vision, machine learning, deep learning, neural networks, Python, GPU programming, mathematical modeling, or statistics is required. Strong Python programming skills, experience with PyTorch or TensorFlow, excellent English, and good collaborative and independent working skills are expected. Experience with biomedical image analysis, Git, LaTeX, Linux, convolutional/transformer networks, graph neural networks, and explainable AI is considered a merit.
Application deadline: 20 August 2026.
How to apply: submit a CV, degree certificate and transcript, thesis or other scientific writing/publications, contact details for at least two references, and a one-page personal letter describing your motivation, up to three scientific achievements, and earliest possible start date. Apply via Uppsala University’s recruitment system.
Funding details
Temporary full-time PhD employment at 100% according to the Higher Education Ordinance. Salary is listed as fixed salary; no stipend amount is given. The position includes doctoral studies and may include up to 20% departmental duties such as teaching and administration.
What's required
Applicants must meet Uppsala University’s doctoral entry requirements: a Master’s degree in computer science, image analysis and machine learning, engineering physics, data science, applied mathematics, molecular biotechnology engineering, or a related field; or at least 240 higher education credits including 60 credits at Master’s level with an independent project of at least 15 credits; or equivalent knowledge. The specific requirement is at least 90 ECTS in areas relevant to image analysis and machine learning, such as image processing, computer vision, machine learning, deep learning, neural networks, Python, GPU programming, mathematical modeling, or statistics. Strong programming skills in Python and deep learning frameworks (PyTorch/TensorFlow), excellent English communication, creativity, thoroughness, structured problem-solving, collaboration, drive, and independence are expected. Meriting qualifications include biomedical research interest, image analysis in medicine, Git, LaTeX, Linux, convolutional/transformer neural networks, graph-based methods, and explainable AI.
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