Research Engineering/ Scientist Assitant - Computational Biology and Protein Modeling

About the role

The Oden Institute is an organized research unit that fosters interdisciplinary programs in computational sciences and engineering, computational medicine, computational geosciences, mathematical modeling, applied mathematics, data science, artificial intelligence, software engineering, and computational visualization. The University of Texas at Austin is a nationally ranked, tier‑one research institution and one of the largest employers in central Texas. Located in the heart of Austin, a vibrant city frequently ranked among the best places to live and work, UT Austin offers a competitive benefits package (see Benefits section) and an enriching, highly collaborative community. This opportunity is designed for individuals with a strong background in biochemistry, molecular biology, biophysics, or a related field who are interested in applying computational methods to problems in protein structure, molecular interactions, and drug discovery.

Responsibilities

• Research: Applying protein docking and structure‑prediction methods to biologically relevant systems; preparing protein, peptide, antibody, and small‑molecule structures for computational experiments; running, organizing, and analyzing large‑scale computational studies; comparing predictions produced by different docking and structure‑prediction tools; evaluating protein interfaces, binding modes, structural quality, and biological plausibility; working with structural and biological databases such as the Protein Data Bank, UniProt, Pfam, InterPro, and related resources; using tools such as PyMOL, ChimeraX, BLAST, Foldseek, and molecular modeling or docking software; designing computational experiments and selecting appropriate controls, benchmarks, and evaluation metrics; interpreting computational results in the context of known biochemical and experimental data.
• Research documentation and communication: Preparing figures, structural visualizations, reports, and presentations.
• Research coordination and collaboration: Collaborating with machine‑learning researchers to identify model limitations and guide further development; working with experimental collaborators to propose testable