Research Fellow, Biochemistry & Molecular Biology

University of Texas Medical Branch (UTMB)•Galveston, TX

About The Position

To conduct advanced, independent, and collaborative research within the Department of Biochemistry & Molecular Biology, contributing to the design, prototyping, and evaluation of computational and artificial intelligence systems that support biological discovery. The candidate will design, prototype, and evaluate artificial intelligence systems at the interface of biological data analysis and large language models. Work will focus on developing computational approaches that combine domain-specific biological knowledge, structured scientific datasets, and modern LLM-based reasoning or retrieval systems.

Requirements

Master’s degree in bioinformatics, computational biology, computer science, genomics, biochemistry, molecular biology, or a related field, or equivalent research experience.

Nice To Haves

Experience working across genomics, scientific data, and modern AI.
Hands-on involvement in designing, prototyping, and evaluating tools that accelerate biological discovery while upholding scientific rigor and reliable grounding in source material.

Responsibilities

Conducts independent and collaborative research under the direction of the principal investigator.
Designs and carries out experiments and computational analyses.
Analyzes and interprets research data and verifies results.
Prepares manuscripts, technical documentation, and presentations for publication and scientific meetings.
Maintains accurate research records and documentation.
Mentors students and junior staff as needed.
Adheres to institutional research, safety, and compliance policies.
Performs related duties as required.
Develop architectures for AI systems that integrate biological datasets, scientific literature, experimental metadata, and large-language-model capabilities. These systems may include retrieval-augmented generation, agentic workflows, structured reasoning pipelines, or domain-specific interfaces for biological research.
Identify, organize, and prepare relevant biological data sources for use in AI workflows. These may include genomic, epigenomic, proteomic, imaging, structural biology, or literature-derived datasets, depending on project needs.
Design and implement LLM-powered tools for scientific question answering, hypothesis generation, literature analysis, experimental planning, data interpretation, and automated report generation. The candidate will evaluate model outputs for scientific accuracy, traceability, and usability.
Build working prototypes, scripts, notebooks, APIs, or lightweight applications demonstrating the proposed AI systems. Prototypes should be documented sufficiently to allow the project team to review, test, and further develop them.
Develop practical evaluation criteria for biological and scientific AI systems, including accuracy, reproducibility, citation grounding, failure modes, hallucination risk, and usefulness to researchers. The candidate will test systems on representative biological use cases and summarize results.
Prepare clear technical documentation describing system architecture, data inputs, model choices, workflows, limitations, and recommended next steps. Documentation should be suitable for internal scientific and technical review.
Meet periodically with project leadership and relevant scientific or computational collaborators to review progress, refine priorities, and incorporate feedback.