Deep Learning Research Assistant — Structural Biology

The University of Texas at Austin; Austin (TX); On-site; USD 30,000 - 36,000; Full time

Benefits offered by this job

100% employer-paid basic medical

Retirement contributions

Paid vacation and sick time

Paid holidays

Job summary

The University of Texas at Austin is seeking a Research Engineering/ Scientist Assistant with strong deep learning expertise for applications in structural biology. The role is based at UT Main Campus within the Oden Institute and involves 20 hours per week on a limited-duration term project.

The candidate will develop and evaluate deep learning methods for protein structures, collaborate with biophysics researchers, and contribute to publications and reports.

Qualifications

• Relevant education and experience may be substituted as appropriate.
• Interest or background in biological or structural applications.

Responsibilities

• Developing and applying deep learning models for predicting protein structures and molecular interactions.
• Exploring model architectures and training approaches, including geometric deep learning, diffusion models, and graph neural networks.
• Preparing and processing biological and structural datasets for model training and evaluation.
• Training, fine-tuning, and evaluating models using GPU and high-performance computing resources.
• Documenting methods and experiments to support reproducibility.
• Collaborating with researchers in computational biology, biochemistry, and biophysics to guide model development and interpret results.

Job description

The University of Texas at Austin is seeking a Research Engineering/ Scientist Assistant with strong deep learning expertise for applications in structural biology. The role is based at UT Main Campus within the Oden Institute and involves 20 hours per week on a limited-duration term project.

The candidate will develop and evaluate deep learning methods for protein structures, collaborate with biophysics researchers, and contribute to publications and reports.