Research Engineering/ Scientist Assitant - Computational Biology and Protein Modeling

University of Texas; Utah; On-site; USD 28,000 - 39,000; Part time

Job summary

The University of Texas at Austin's Oden Institute invites applications for a Research Engineering/ Scientist Assistant in Computational Biology and Protein Modeling. The role focuses on applying computational methods to protein structure, molecular interactions, and drug discovery.

Responsibilities include running large-scale studies, preparing structures, and collaborating with experimental teams and machine-learning researchers to validate predictions and share findings.

Qualifications

• Bachelor's degree in biochemistry, molecular biology, or related field.
• Understanding of protein structure, folding, and interactions.
• Experience with visualization tools (PyMOL/ChimeraX) and databases.
• Familiarity with Python and Linux for scripting and computation.

Responsibilities

• Apply protein docking and structure-prediction methods to systems.
• Prepare protein, peptide, antibody, and small-molecule structures.
• Run and analyze large-scale computational studies.
• Compare predictions from different docking tools.
• Collaborate with ML researchers to identify model limits.
• Prepare figures, reports, and presentations.

Skills

Biochemistry background

Molecular biology

Biophysics

Bioinformatics

Python scripting

Linux proficiency

Education

Bachelor's Degree in a relevant field

Tools

PyMOL

ChimeraX

BLAST

Foldseek

Molecular modeling software

Job description

Job Posting Title: Research Engineering/ Scientist Assitant - Computational Biology and Protein Modeling

Hiring Department: Oden Institute

Position Open To: All Applicants

Weekly Scheduled Hours: 20

FLSA Status: To Be Determined at Offer

Earliest Start Date: Ongoing

Position Duration: Limited Duration Based on Business Need

Location: UT MAIN CAMPUS

Job Details: General Notes

The Oden Institute is an organized research unit that fosters interdisciplinary programs in computational sciences and engineering, computational medicine, computational geosciences, mathematical modeling, applied mathematics, data science, artificial intelligence, software engineering, and computational visualization. The University of Texas at Austin is a nationally ranked, tier-one research institution and one of the largest employers in central Texas. UT is located in the heart of Austin, a vibrant city that frequently appears on lists of best cities to live and work. Committed to recruiting and retaining a varied and talented workforce, the university offers competitive salaries and benefits, an extensive support network, and above all, an enriching and highly collaborative community that is deeply passionate about our vision for higher education and public service. UT Austin offers a competitive benefits package that includes: 100% employer-paid basic medical coverage Retirement contributions Paid vacation and sick time Paid holidays Please visit our Human Resources (HR) website to learn more about the total benefits offered. NOTE: This position is initially appointed for a six-month term. Continuation beyond the initial six-month assignment is contingent upon funding availability and satisfactory performance.

Purpose

We are inviting highly motivated individual to join our research group at the Oden Institute, University of Texas at Austin. This opportunity is designed for individuals with a strong background in biochemistry, molecular biology, biophysics, or a related field who are interested in applying computational methods to problems in protein structure, molecular interactions, and drug discovery.

Responsibilities

• Research Applying protein docking and structure-prediction methods to biologically relevant systems
• Preparing protein, peptide, antibody, and small-molecule structures for computational experiments
• Running, organizing, and analyzing large-scale computational studies
• Comparing predictions produced by different docking and structure-prediction tools
• Evaluating protein interfaces, binding modes, structural quality, and biological plausibility
• Working with structural and biological databases such as the Protein Data Bank, UniProt, Pfam, InterPro, and related resources
• Using tools such as PyMOL, ChimeraX, BLAST, Foldseek, and molecular modeling or docking software
• Designing computational experiments and selecting appropriate controls, benchmarks, and evaluation metrics
• Interpreting computational results in the context of known biochemical and experimental data
• Research documentation and communication
• Preparing figures, structural visualizations, reports, and presentations
• Research coordination and collaboration
• Collaborating with machine-learning researchers to identify model limitations and guide further development
• Working with experimental collaborators to propose testable hypotheses and prioritize computational predictions for validation

Required Qualifications

• Bachelor's Degree in a relevant or related field
• A strong background in biochemistry, molecular biology, structural biology, biophysics, bioinformatics, or a related discipline
• A solid understanding of protein structure and function, including:
• Amino acid properties and protein folding
• Protein domains and conformational changes
• Protein–protein and protein–ligand interactions
• Noncovalent interactions, binding interfaces, and molecular recognition
• Experience using PyMOL, ChimeraX, or similar molecular visualization software
• Familiarity with biological and structural databases
• Ability to analyze scientific literature and connect computational predictions with biological mechanisms
• Basic scripting or programming experience, preferably in Python
• Comfort working in Linux environments and running computational tools from the command line
• Strong organizational skills and the ability to manage multiple computational experiments and datasets
• Careful attention to structural quality, data interpretation, and reproducibility

Relevant education and experience may be substituted as appropriate.

Preferred Qualifications

Prior experience with protein docking, molecular dynamics, structural alignment, virtual screening, high-performance computing, or machine-learning-based structure-prediction tools is helpful but not required. Training will be provided, but candidates should be motivated to learn new computational methods and work independently.

Salary Range $3,000 monthly (50% Full Time Equivalent)

Working Conditions May work around standard office conditions Repetitive use of a keyboard at a workstation Use of manual dexterity Required

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

A brief description of your research interests and relevant experience, and any examples of previous computational or structural biology work.

Employment Eligibility

Regular staff who have been employed in their current position for the last six continuous months are eligible for openings being recruited for through University-Wide or Open Recruiting, to include both promotional opportunities and lateral transfers. Staff who are promotion/transfer eligible may apply for positions without supervisor approval.

Retirement Plan Eligibility

The retirement plan for this position is Teacher Retirement System of Texas (TRS), subject to the position being at least 20 hours per week and at least 135 days in length.

Background Checks

A criminal history background check will be required for finalist(s) under consideration for this position.

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.

Pay Transparency

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. However, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by the employer, or (c) consistent with the contractor’s legal duty to furnish information.

Employment Eligibility Verification

If hired, you will be required to complete the federal Employment Eligibility Verification I-9 form. You will be required to present acceptable and original documents to prove your identity and authorization to work in the United States. Documents need to be presented no later than the third day of employment. Failure to do so will result in loss of employment at the university.

E-Verify

The University of Texas at Austin use E-Verify to check the work authorization of all new hires effective May 2015. The university’s company ID number for purposes of E-Verify is 854197. For more information about E-Verify, please see the following: E-Verify Poster (English and Spanish) [PDF] Right to Work Poster (English) [PDF] Right to Work Poster (Spanish) [PDF]

Compliance

Employees may be required to report violations of law under Title IX and the Jeanne Clery Disclosure of Campus Security Policy and Crime Statistics Act (Clery Act) and to be notified of the availability of the Annual Security and Fire Safety report. You may access the most recent report here or obtain a copy at University Compliance Services, 1616 Guadalupe Street, UTA 2.206, Austin, Texas 78701.

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