Research Assistant

Overview Research Assistant role in the Oden Institute with a focus on preparation and execution of ultra-large protein structure modeling runs and customization of DeepLearning- and Physics-based molecular structure prediction tools. Part-time position (50% FTE; 20 scheduled weekly hours) with an anticipated end date of December 31, 2026. Location: UT Main Campus. 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 (e.g., Boltz, AlphaFold3). 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. Required Qualifications Bachelor’s degree in Computational Biology, Computer Science, Bioinformatics, or related quantitative/scientific field. 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). Relevant education and experience may be substituted as appropriate. Preferred Qualifications Experience with modeling protein interactions using physics-based approaches (ClusPro, Rosetta). Experience in High-Performance Computing (HPC) environments (Slurm, CUDA) 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 keyboard use at a workstation. Occasional weekend, overtime and evening work to meet deadlines. Required Materials Resume/CV 3 work references with contact information; at least one reference from a supervisor. Letter of interest Important applicant guidance Applicants not currently employed by the University of Texas at Austin may be prompted to upload a resume and additional materials during the application process. Current UT employees should apply through Workday and may need to update their Professional Profile and respond to application questions to upload any additional materials. Employment Eligibility & Compliance Employment Eligibility: Regular staff who have been employed in their current position for the last six months are eligible for openings recruited through University-Wide or Open Recruiting, including promotional opportunities and lateral transfers. Employment is subject to background checks and federal/state compliance requirements. E-Verify and related compliance information are provided by UT Austin as part of the onboarding process. Equal Opportunity Employer The University of Texas at Austin is an equal opportunity/affirmative action employer 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. #J-18808-Ljbffr