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Postdoctoral Researcher – AI Foundation Models, Breast Cancer Recurrence
ScreenPoint MedicalPostdoctoral Researcher developing AI Foundation Models for breast cancer recurrence at ScreenPoint Medical. Collaborate with the University of Copenhagen's Innovation team in an interdisciplinary environment in Copenhagen.
Tech Stack
Tools & technologiesLinuxPythonPyTorch
About the role
Key responsibilities & impact- Design and develop robust foundation and world models (e.g. vision transformers, vision-language models, LeJepa) tailored to breast imaging data.
- Apply and adapt foundation models to downstream tasks focused on breast cancer detection and risk in women with earlier diagnosed cancers.
- Collaborate closely with ScreenPoint's AI Algorithm Lead and AI Research Engineers to ensure robust integration between foundation models, MLOps infrastructure, and downstream evaluation metrics.
- Perform and report on in-depth model analysis, including calibration, subgroup performance, and failure-mode assessment.
- Publish findings in high-impact peer-reviewed journals and present at leading international conferences, supported by your academic supervisor at the University of Copenhagen.
Requirements
What you’ll need- PhD in Computer Science, Machine Learning, Biomedical Engineering, Applied Mathematics, Physics, Statistics or a closely related technical field.
- Proven research experience with foundation models and state-of-the-art deep learning methods (vision transformers, vision-language models, self-supervised learning).
- Publication track record.
- Experience in medical imaging or clinical AI research is strongly preferred.
- Ability to translate clinical or scientific questions into appropriate modeling approaches (e.g. classification, risk prediction, longitudinal modeling).
- Strong understanding of model evaluation, calibration, robustness, and subgroup performance in real-world datasets.
- Proficiency in Python and deep learning frameworks (e.g. PyTorch), and experience working in Linux-based environments and GPU clusters.
- Excellent written and spoken English and the ability to work both independently and collaboratively.
- Experience with breast data (e.g. xray, MRI).
- Familiarity with NLP, LLMs, or vision-language models (VLMs).
- Experience with self-supervised or weakly supervised learning on large-scale datasets.
- Prior publications in top-tier AI or medical imaging venues (NeurIPS, ICCV, MICCAI, CVPR, etc.).
Benefits
Comp & perks- Access to large-scale, high-quality clinical breast imaging datasets through ScreenPoint's industry partnerships and clinical collaborations.
- A collaborative, ambitious, and international environment spanning both industry and academia.
ATS Keywords
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Hard Skills & Tools
foundation modelsdeep learningvision transformersvision-language modelsself-supervised learningmodel evaluationcalibrationrisk predictionPythonPyTorch
Soft Skills
excellent written and spoken Englishindependent workcollaborative work
Certifications
PhD in Computer SciencePhD in Machine LearningPhD in Biomedical EngineeringPhD in Applied MathematicsPhD in PhysicsPhD in Statistics