(ID: 2026-1886)
Position Overview
NGRF (
NovaGen
Research Fund) is a new data science collaboration hub including multiple labs and centers.
NGRF is seeking a researcher to join the LLM Lab and contribute to NGRF’s scientific discovery platform. This role combines original research in scientific integrity with hands-on development of NLP and AI capabilities on a large-scale biomedical data platform.
The researcher will work with a multi-terabyte curated literature database, develop and evaluate NLP models, build retrieval-augmented generation (RAG) pipelines, and contribute to a federated knowledge graph that models scientific claims and evidence relationships. The role spans research and engineering — publishing peer-reviewed findings while building the models and pipelines that power NGRF’s platform.
Key Responsibilities
- Scientific integrity research
: Design and execute large-scale computational analyses of scientific publishing practices across the biomedical literature. Publish findings in peer-reviewed venues (e.g., Scientometrics, JASIST, Quantitative Science Studies).
- NLP model development:
Develop and validate models for semantic analysis of scientific text — including claim extraction, relevance scoring, and relationship detection. Build on existing RAG infrastructure and vector search capabilities.
- Big data and pipeline engineering:
Work with a multi-terabyte literature database and external APIs (PubMed, CrossRef, OpenAlex) to build scalable data processing and analysis pipelines. Integrate structured and unstructured data sources into reproducible workflows.
Required Qualifications
- PhD in natural language processing, computational linguistics, computer science, information science, or a related field
- Publication record in NLP, text mining, or computational approaches to scientific literature
- Strong programming skills in Python with experience in modern ML/NLP frameworks (e.g., PyTorch, Hugging Face Transformers)
- Experience working with large text corpora and large-scale datasets
- Familiarity with vector databases, embeddings, semantic search, and retrieval-augmented generation (RAG)
- Experience with knowledge graphs, ontology design, or graph databases
- Demonstrated ability to design and execute independent research projects
- Strong written and oral communication skills
Preferred Qualifications
- Background in scientometrics, bibliometrics, or research integrity
- Experience with scientific publishing APIs and metadata (DOIs, PubMed, CrossRef, OpenAlex)
- Familiarity with claim extraction, relation extraction, or scientific argument mining
- Proficiency with Git, GitHub, and collaborative software development workflows
Compensation
The prospective salary range for this position is $80,000–$92,000 annually.
Salary Range: $80,000 USD - $92,000 USD
Disclaimer:
The above description is meant to illustrate the general nature of work and level of effort being performed by individuals assigned to this position or job description. This is not restricted as a complete list of all skills, responsibilities, duties, and/or assignments required. Individuals may be required to perform duties outside of their position, job description or responsibilities as needed.
The diversity of NovaGen's employees is a tremendous asset. We are firmly committed to providing equal opportunity in all aspects of employment and will not tolerate any illegal discrimination or harassment based on age, race, gender, religion, national origin, disability, marital status, covered veteran status, sexual orientation, status with respect to public assistance, and other characteristics protected under state, federal, or local law and to deter those who aid, abet, or induce discrimination or coerce others to discriminate.
Accessibility: If you need an accommodation as part of the employment process please contact:
info@novagenresearch.org
This role has a market-competitive salary with an anticipated base compensation range listed below. Actual salaries will vary depending on a candidate’s experience, qualifications, skills, and location.
