Research Fellow, Biochemistry & Molecular Biology

Posted 2 days ago

university of texas medical branch healthGalveston (TX)

Biological Scientists, All Other Research and Development in Biotechnology (except Nanobiotechnology)

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About the role

Research FellowTo 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.

Job Duties and Responsibilities: Core duties:

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. Project responsibilities may include: System design. 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. Biological data integration. 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. LLM-based workflow development. 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. Prototype implementation. 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. Evaluation and validation. 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. Documentation and recommendations. 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. Collaboration. Meet periodically with project leadership and relevant scientific or computational collaborators to review progress, refine priorities, and incorporate feedback. Deliverables The candidate will provide one or more of the following, as requested by the project team:
Technical design documents for AI systems spanning biology and LLMs.
Prototype software, notebooks, scripts, or application components.
Curated or structured biological data inputs for AI workflows.
Evaluation reports describing system performance, limitations, and risks.
Written recommendations for future development, deployment, or publication.
Periodic progress summaries.

Expected Outcome The work is expected to produce practical designs and early-stage prototypes for AI systems that support biological research using large language models, with emphasis on scientific rigor, interpretability, reliable grounding in source material, and usability by researchers.

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