Design and implement AI/NLP methods for biomedical literature mining and structured protocol knowledge extraction.
Develop benchmark datasets, annotation guidelines, and evaluation pipelines for scientific information extraction.
Build and evaluate RAG, in-context learning, fine-tuning, graph matching, entity normalization, and KG query workflows.
Analyze extraction errors, model behavior, retrieval failures, grounding quality, and biological ambiguity.
Collaborate with software engineers to integrate research methods into usable tools and reproducible pipelines.
Collaborate with organoid biologists and domain experts to translate biological protocol knowledge into computable representations.
Prepare manuscripts, conference abstracts, technical reports, design documents, and open-source research artifacts.
Help define research milestones, evaluation criteria, and publication strategy for protocol intelligence work.

PhD in computer science, computational biology, bioinformatics, biomedical informatics, NLP, machine learning, data science, or a related field.
Strong Python programming skills.
Demonstrated research experience with NLP, information extraction, LLMs, RAG, transformers, structured prediction, or scientific text mining.
Ability to design controlled computational experiments, create benchmark datasets, and analyze results rigorously.
Familiarity with biological, biomedical, or scientific data.
Strong written communication skills and interest in publishing methods-oriented research.
Comfort working with complex, evolving research codebases and interdisciplinary teams.

Data Science Fellow – AI, NLP

Key skills