Arbeidsoppgaver
- Developing representation learning algorithms for whole-slide digital pathology images.
- Utilize and/or develop machine learning models and algorithms on histological images to detect and quantify immunogenic components as prognostic and predictive biomarkers for tailored cancer treatment is breast cancer.
- Implement methods for the multimodal integration of orthogonal and heterogeneous data sources, such as histopathology, genomics, transcriptomics and clinical variables.
- Engage in an ongoing international collaboration with Dana-Farber Cancer Institute and Harvard Medical School, as featured in our recent JAMA Oncology publication (PMID: 39724105), with opportunities for active participation.
- For Norwegian candidates, the funding body requires an international research stay of 3 to 12 months, which will be fully supported by a dedicated mobility grant. This requirement does not apply to international candidates.
- The project includes patient data from two major hospitals in Norway: Oslo University Hospital (OUS) and UNN. The candidate may be required to visit OUS for data collection and project-related activities.
- The candidate will participate in several regular meetings at UNN/UiT, including making work-in-progress presentations; and more formal presentations at national and international conferences; and will prepare manuscripts as appropriate.
- This position is funded by the Northern Norway Regional Health Authority.
The project leader is Dr. Mehrdad Rakaee. Co-mentors: Prof. Lill-Tove Rasmussen Busund (UNN/UiT) and Prof. Åslaug Helland (OUS).
Kvalifikasjoner
Essential:
- Applicants must hold a degree equivalent to a Norwegian doctoral degree (PhD) in computer science, data science, biostatistics or a related field.
- Strong background in machine learning, statistics and deep learning.
- Proficiency in python and bash programming.
- Experience with machine learning frameworks (such as TensorFlow/Keras and PyTorch).
- Experience with deep learning statistical modelling like Neural Networks, CNNs, ViT, GANS etc.
Preferred:
- Prior experience in computer vision, particularly in pathology image analysis or other biomedical imaging domains.
- Familiarity with self-supervised learning and pathology foundation models.
- Experience with statistical analyses in R.
- Documented research experience.
Personlige egenskaper
- High motivation and commitment.
- Ability to work both independently and in teams.
- Good collaboration and communication skills.
- Ability and interest to (co)supervise students (MSc, PhD).
- Proactive, flexible, and “can-do” attitude.
- Good written and oral English language skills.
Application requirements
- To be considered, please submit the following:
- A motivation letter (1 page) explaining your interest and suitability for the position
- A full CV, including publications (preprints accepted)
- PhD diploma, thesis abstract and academic transcripts. If the PhD degree is not yet awarded, please include a confirmation of thesis submission and the full thesis document.
- Contact details for two recent referees (name, email, phone and relation)
Incomplete applications will not be reviewed. Only applications submitted via Webcruiter will be considered. Applicants with foreign degrees should include a brief explanation of their university’s grading system. All documents must be in English or a Scandinavian language.
Vi tilbyr
- A central role for the introduction of personalized oncology in the healthcare system.
- An exciting workplace with good opportunities for professional and personal development.
- Good working environment with an emphasis on internal training and skills development.
- Close and good collaboration with other departments and clinics.
- Salary according to standard wage agreement.
- Membership in pension schemes and relevant insurance coverage.