Faculty-advised research — team forming
→ Core contributors included as co-authors
Evidence Retrieval AI
Evidence retrieval for resource-limited clinical settings where standard access infrastructure is unavailable. The gap between published literature and frontline care decisions is the core constraint. Research conducted with faculty advisors at Boston University.
Clinical evidence retrieval fails in underserved settings because the default stack assumes resources that are not present: paid databases, librarians, and time for manual search. The access gap between published literature and frontline clinical decisions is structural, not incidental. This thread studies that constraint in low-resource clinical environments.
Apply by April 30, 2026
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Apply Now →AI Research Engineer
OpenRuns experiments, implements baselines, and benchmarks retrieval across specialist agents. Builds provenance from source through synthesis.
Skills: Python, Pinecone, OpenAI Embeddings, AWS, RAG Systems, Evaluation
Apply →Data & Statistical Analyst
OpenHandles evaluation datasets, statistical validation of retrieval quality, and quantitative comparison against PubMed and RAG baselines.
Skills: Statistics, Medical corpora, Evaluation metrics, Data pipelines
Apply →Research Writer
OpenLiterature review, draft paper sections, and format citations for clinical AI and medical informatics venues.
Skills: Academic Writing, Medical literature, Citations, AMIA / npj-style formatting
Apply →Research Coordinator
OpenTracks sprint progress, meeting notes, and keeps the collaboration thread organized across engineers and advisors.
Skills: Project coordination, Documentation, Async communication
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