Assistant Research Scientist; PREP

PREP Research Associate

This position is part of the National Institute of Standards and Technology (NIST) Professional Research Experience Program (PREP). NIST encourages collaborative research between its staff and academic institutions on projects of mutual interest. The PREP student will perform technical work supporting the collaboration's scientific research.

Multi-Agent Consensus System for AI-Driven Antimicrobial Peptide Discovery

The PREP student will work with researchers in the Applied AI Research Group to develop a multi-agent consensus system that transforms AI-generated text into quantifiable scientific signals for antimicrobial peptide discovery. We have developed STAMP (Species- and Topic-aware Representation Learning for AMP Discovery), a machine learning framework that predicts antimicrobial effectiveness. The project challenges how to translate STAMP’s explainability heatmaps into high-confidence wet‑lab decision making.

The student will engineer an ensemble of independent AI agents that analyze STAMP’s heatmaps, generate structured “Hypothesis Cards,” evaluate semantic consistency, and produce consensus scores to gate wet lab experiments.

• Assist in building an ensemble of hypothesis generation agents using diverse large‑language‑model architectures (Claude, GPT‑4, Llama variants).
• Develop linguistic evaluation metrics to measure semantic consistency across agent outputs, including mechanism entailment, evidence overlap, and experiment design similarity.
• Create parsers to transform STAMP’s Gradient × Input explainability heatmaps into structured hypothesis cards.
• Implement the end‑to‑end pipeline from STAMP heatmaps to hypothesis cards to consensus reports.
• Benchmark the system against historical experimental data.
• Contribute to scientific manuscripts and technical documentation.
• Develop production‑ready code using Python and ML/AI frameworks, applying version control and testing best practices.

• Bachelor of Science degree in Computer Science (CS) or enrolled in a Master’s/Ph.D. program in CS, Computational Biology, Bioinformatics, or a related field.
• Proficiency in Python programming.
• Experience with machine learning frameworks such as PyTorch, Tensor Flow, or JAX.
• Understanding of natural language processing techniques.
• Familiarity with version control (Git) and software engineering best practices.
• Knowledge of deep learning architectures and statistical analysis.
• Willingness to collaborate closely with wet‑lab researchers, biophysicists, and domain experts.
• Strong interest in bridging AI/ML with biomedical applications.
• Excellent communication skills for explaining computational concepts to non‑CS audiences.
• Comfort working in interdisciplinary research environments.
• Self‑motivated with strong problem‑solving abilities.
• Strong oral and written communication skills.

The referenced salary range represents the minimum and maximum salaries for this position and is based on Johns Hopkins University’s good faith belief at the time of posting. Actual compensation may vary depending on location, skills, experience, internal equity, market conditions, and education/training.

Johns Hopkins offers a comprehensive benefits package supporting health, life, career, and retirement. More information can be found in the university’s benefits portal.

The Johns Hopkins University is committed to equal opportunity for faculty, staff, and students. The university does not discriminate on the basis of sex, gender, marital status, pregnancy, race, color, ethnicity, national origin, age, disability, religion, sexual orientation, gender identity, or veteran status. Qualified individuals are provided access to all academic and employment programs based on demonstrated ability, performance, and merit.

For more information, see the Equal Employment Opportunity Commission website.