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Research Engineering/ Scientist Associate II – Computational Research

🏛️ The University of Texas at Austin Staff 📍 United States (US) 🗓️ Posted 1 day ago

About this role

Research Engineering/ Scientist Associate II – Computational Research

The Savinov lab at UT Austin is pursuing multiple projects centered around discovering and designing protein fragments as universal regulators of protein interactions in health and disease – including in the contexts of antibiotic resistance, cell migration, neurodegeneration, and cancer. The lab seeks a highly motivated, curious, and organized individual to join the team and contribute to multiple research projects, working both individually and as part of a group. The candidate will set up and perform computational predictions and develop novel computational approaches supporting research efforts in the lab, and also be involved in training new lab members in these techniques. Computational approaches will include AI/ML approaches, protein design methods, bioinformatics, and data science methods to handle large experimental and computational datasets. The candidate should have experience with working on high-performance computing systems. Additional experience in experimental biology is favorable, but not required.

Responsibilities

  • Computational research and research support. Support and perform computational research to discover and design protein fragments as regulators of cellular protein interactions, both individually under the guidance of the PI and as part of a group. Computational approaches will include AI/ML-based structure prediction, protein design, and bioinformatics approaches, as well as data science to handle and extract insights from large experimental datasets. Perform routine computational experiments and develop novel approaches to address research problems. Maintain the lab's computational infrastructure. Maintain careful records.
  • Training and general lab support. Assist with maintaining shared resources, protocols and documentation; help train new laboratory members computational methods; assist with additional research and operational needs as appropriate.

    Required Qualifications

    • Master's degree in a related field, or bachelor's degree plus at least two years of relevant experience.
    • Experience with and willingness to perform routine and novel computational methods, including on high-performance computing environments.
    • Experience with AlphaFold and related AI/ML structure-prediction approaches.
    • Experience with Python, BASH, and high-performance computing systems.
    • Experience with data science and working with large experimental and computational datasets.
    • Experience with AI dev tools. Experience with learning new computational and AI/ML techniques and a strong desire to continue to do so.
    • Demonstrated reliability, attention to detail, and ability to maintain accurate records and follow established procedures.
    • Ability to work both independently and as part of a group, and recognize and respond to problems in a timely manner. Strong organizational and communication skills.

      Preferred Qualifications

      • Experience with managing shared codebases via GitHub is desirable.
      • Experience with setting up and managing shared computational infrastructure.
      • Additional experience with experimental biology.

        Salary Range

        $45,000+ depending on qualifications

        Working Conditions

        • Uniforms and/or personal protection equipment (furnished).
        • May work around chemical fumes.
        • May work around standard office conditions.
        • May work around biohazards.
        • May work around chemicals.

          Required Materials

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

Details

Organization

The University of Texas at Austin Staff

Location

United States (US)

Position type

Research

Posted

September 19, 2026

Last seen on the employer's board

September 20, 2026

Source

jobRxiv

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