Data Science Fellow - AI/NLP
Core
Develop AI/NLP and knowledge engineering methods to transform biomedical literature and experimental protocols into structured, queryable knowledge for organoid protocol standardization.
Role type
Postdoctoral researcher (AI/NLP & Knowledge Engineering)
Builds
Structured biological knowledge, benchmark datasets, and reproducible research pipelines
Domain
Biomedical informatics, computational biology, NLP
Deliverable
production ML models | research
Required skills
Python programming, NLP, information extraction, LLMs, RAG, transformers, structured prediction, scientific text mining, controlled computational experiments, benchmark dataset design
Preferred skills
Scientific document processing, PDF parsing, knowledge graphs, ontologies, graph databases, fine-tuning LLMs, LoRA/QLoRA, Hugging Face, PyTorch, prompt engineering, agentic LLM workflows, vector search, bioinformatics concepts
Technologies
Python, LLMs, RAG, transformers, PyTorch, Hugging Face, graph databases, vector search
Responsibilities
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
Seniority
Postdoctoral researcher, research-focused IC
