Research Engineering/ Scientist Assitant - Computational Biology and Protein Modeling

The University of Texas at Austin•Austin, UT

1d•Onsite

About The Position

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. UT is located in the heart of Austin, a vibrant city that frequently appears on lists of best cities to live and work. Committed to recruiting and retaining a varied and talented workforce, the university offers competitive salaries and benefits, an extensive support network, and above all, an enriching and highly collaborative community that is deeply passionate about our vision for higher education and public service. UT Austin offers a competitive benefits package that includes: 100% employer-paid basic medical coverage Retirement contributions Paid vacation and sick time Paid holidays Please visit our Human Resources (HR) website to learn more about the total benefits offered. NOTE: This position is initially appointed for a six-month term. Continuation beyond the initial six-month assignment is contingent upon funding availability and satisfactory performance. We are inviting highly motivated individual to join our research group at the Oden Institute, University of Texas at Austin. 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.

Requirements

Bachelor's Degree in a relevant or related field
A strong background in biochemistry, molecular biology, structural biology, biophysics, bioinformatics, or a related discipline
A solid understanding of protein structure and function, including: Amino acid properties and protein folding Protein domains and conformational changes Protein–protein and protein–ligand interactions Noncovalent interactions, binding interfaces, and molecular recognition
Experience using PyMOL, ChimeraX, or similar molecular visualization software
Familiarity with biological and structural databases
Ability to analyze scientific literature and connect computational predictions with biological mechanisms
Basic scripting or programming experience, preferably in Python
Comfort working in Linux environments and running computational tools from the command line
Strong organizational skills and the ability to manage multiple computational experiments and datasets
Careful attention to structural quality, data interpretation, and reproducibility
Relevant education and experience may be substituted as appropriate.

Nice To Haves

Prior experience with protein docking, molecular dynamics, structural alignment, virtual screening, high-performance computing, or machine-learning-based structure-prediction tools is helpful but not required.
Training will be provided, but candidates should be motivated to learn new computational methods and work independently.

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 hypotheses and prioritize computational predictions for validation