Research Assistant

About the role

A Research Assistant role with a focus on preparation and execution of ultra-large protein structure modeling runs and customization of Deep Learning- and Physics-based molecular structure prediction tools to enable modeling on this scale. This is a part-time position (50% FTE; 20 scheduled weekly hours) with an anticipated end date of December 31, 2026.

Responsibilities

• Model Execution & Pipeline Optimization: Prepare, scale, and execute ultra-large protein structure modeling workflows for massive systems (>8000 residues). Adapt, optimize, and troubleshoot existing deep learning and physics-based pipelines to handle extreme scale without memory or performance bottlenecks.
• Tool Customization & Finetuning: Finetune and customize cutting-edge protein structure prediction models (like Boltz, AlphaFold3, etc.). Implement custom scripting to bridge physics-based and machine-learning methodologies.
• Data Management & Analysis: Featurize structural data, manage large datasets, and analyze structural modeling outputs for quality and biological relevance. Maintain rigorous documentation of code, workflows, and benchmarking results.
• Collaborative Support: Assist team members with computational setups, software dependencies, and cluster/cloud environment configurations.

Requirements

• Education: Bachelor’s degree in Computational Biology, Computer Science, Bioinformatics, or a related quantitative/scientific field.
• Experience: Experience with featurizing and performing structure modeling for ultra-large protein systems (>8000 residues). Experience with training and fine tuning of protein structure prediction models such as Boltz and AlphaFold3. Confident scripting skills (Python, Bash). Familiarity with deep learning frameworks (PyTorch, PyTorch Lightning).

Preferred Qualifications

• Experience: Experience with modeling protein interactions using Physics-based approaches (ClusPro, Rosetta, etc.). Experience working in High-Performance Computing (HPC) environments (Slurm, CUDA execution) or cloud infrastructure (AWS/GCP) optimized for multi-GPU training. Familiarity with containerization tools (Docker, Singularity) for reproducible workflows.

Salary

$3,000 monthly. Part-time, 50% FTE (20 Scheduled Weekly Hours)

Working Conditions

May work around standard office conditions. Repetitive use of a keyboard at a workstation. Occasional weekend, overtime and evening work to meet deadlines.

Required Materials

• Resume/CV
• 3 work references with their contact information; at least one reference should be from a supervisor.

Important for applicants who are NOT current university employees or contingent workers:

You will be prompted to submit your resume the first time you apply, then you will be provided an option to upload a new Resume for subsequent applications. Any additional Required Materials (letter of interest, references, etc.) will be uploaded in the Application Questions section; you will be able to multi-select additional files. Before submitting your online job application, ensure that ALL Required Materials have been uploaded. Once your job application has been submitted, you cannot make changes.

Equal Opportunity Employer

The University of Texas at Austin, as an equal opportunity/affirmative action employer, complies with all applicable federal and state laws regarding nondiscrimination and affirmative action. The University is committed to a policy of equal opportunity for all persons and does not discriminate on the basis of race, color, national origin, age, marital status, sex, sexual orientation, gender identity, gender expression, disability, religion, or veteran status in employment, educational programs and activities, and admissions.