Research Engineering/ Scientist Associate II – Computational Research
University of Texas at Austin · Austin, TX, US
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About the Role
The Savinov lab at UT Austin is seeking a motivated individual to contribute to research projects focused on discovering and designing protein fragments as universal regulators of protein interactions. The role involves setting up and performing computational predictions, developing novel computational approaches, and training new lab members. Computational methods will include AI/ML, protein design, bioinformatics, and data science, with experience on high-performance computing systems required.
Responsibilities
- Perform computational research and research support for discovering and designing protein fragments.
- Utilize AI/ML-based structure prediction, protein design, and bioinformatics approaches.
- Apply data science methods to analyze large experimental datasets.
- Develop novel computational approaches to address research problems.
- Maintain the lab’s computational infrastructure.
- Maintain careful records.
- Assist with maintaining shared resources, protocols, and documentation.
- Help train new laboratory members in computational methods.
- Assist with additional research and operational needs as appropriate.
Requirements
- 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.
- Ability to recognize and respond to problems in a timely manner.
- Strong organizational and communication skills.
- Experience with managing shared codebases via GitHub is desirable.
- Experience with setting up and managing shared computational infrastructure.
- Additional experience with experimental biology.
Skills
- AI/ML
- Protein design
- Bioinformatics
- Data science
- High-performance computing
- Python
- BASH
- AlphaFold
- AI dev tools
- GitHub
Location
- AUSTIN, TX
Work Type
- Full-time
Experience Level
- Master’s degree or Bachelor's degree with 2+ years of experience
Education Level
- Master's degree
- Bachelor's degree
Salary/Compensations
- $45,000+
Benefits
- Teacher Retirement System of Texas (TRS) eligibility if position is at least 20 hours per week and 135 days in length.
- Uniforms and/or personal protection equipment furnished.
About the Company
- The University of Texas at Austin is an equal opportunity/affirmative action employer.
- 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.
- The University of Texas at Austin will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant.
- If hired, you will be required to complete the federal Employment Eligibility Verification I-9 form.
- The University of Texas at Austin uses E-Verify to check the work authorization of all new hires.
Equal Opportunity
- 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.