Postdoctoral Fellow, AI/ML for Tissue Analytics

About the role

This postdoctoral fellowship focuses on AI/ML for tissue analytics within the Tissue Biomarkers & Analytics team at Quantitative Biosciences. You will work in a state‑of‑the‑art research laboratory in Biopolis, Singapore, collaborating across data science, pathology, biology and drug discovery.

Key responsibilities

• Design, build, validate and deploy innovative AI/ML models for digital pathology and tissue imaging data from preclinical and clinical studies.
• Develop advanced machine‑learning methodologies such as foundation models, multimodal learning and generative AI to extract biological insights.
• Formulate scientific hypotheses, execute computational studies and interpret findings in the context of disease biology and biomarker discovery.
• Collaborate with interdisciplinary teams and external partners to advance internal analytical capabilities.
• Disseminate results through peer‑reviewed publications and presentations at scientific conferences.

Required profile

• PhD in Computer Science, Data Science, Biomedical Engineering, Computational Biology, Physics or a related discipline.
• Track record of scientific publications in peer‑reviewed journals or conferences.
• Strong background in machine learning and deep learning, including foundation models, representation learning and multimodal approaches.
• Interest in disease biology and applying computational methods to drug discovery challenges.

Required skills

• Python programming.
• Machine‑learning frameworks: PyTorch, TensorFlow, scikit‑learn.
• Cloud platforms and ML engineering: AWS SageMaker, Databricks (or equivalent).
• Digital pathology and image‑analysis tools: HALO, QuPath, Visiopharm, OpenCV, ITK, SimpleITK.
• Experience with AI techniques such as generative AI, transformers and self‑supervised learning.