Innovation Postdoctoral Fellow, Data Science & AI Vision Foundation Models Cellular Imaging in AI Drug Discovery

Key Responsibilities:

• Develop vision foundation models for high-content cellular imaging using state-of-the-art self-supervised learning.
• Design robust evaluation protocols to measure generalization to unseen assays, perturbations, cell types, and imaging conditions.
• Curate large-scale public and internal microscopy datasets; establish standards for data quality and reproducibility.
• Extend models to multimodal settings by integrating imaging with other modalities (e.g., compound data) for drug discovery/phenotypic profiling.
• Build scalable, reproducible training and inference pipelines across HPC and cloud; maintain clean, well-documented code.
• Propose innovative modeling/evaluation methods; work independently and collaboratively and publish/present results.

• PhD (or equivalent) in a relevant scientific discipline completed prior to start date (PhD conferred in 2026 only).
• Demonstrated scientific achievement (publications, presentations, patents, or equivalent).
• Strong commitment to learning/innovation/professional development.
• Ability to formulate and drive independent research questions.
• Proficiency in Python and modern deep learning frameworks (PyTorch, Tensor Flow, or JAX); hands-on GPU training.
• Experience training on HPC or cloud with reproducible code.
• Working knowledge of vision foundation models (self-supervised learning, vision transformers) with fine-tuning/adaptation.
• Experience designing benchmarking/evaluation protocols; strong written and verbal communication.

• Pretraining vision foundation models from scratch on large-scale imaging datasets.
• Expertise in high-content/biomedical imaging (e.g., Cell Painting, phenomics, digital pathology) and channel-adaptive/multimodal architectures.
• Distributed training on HPC; reproducible ML pipelines on cloud; MLflow.

• Starting salary: $87,000 USD/year; comprehensive benefits (health, life/disability, 401(k) match, time off).