Postdoctoral Fellow, Department of Computer Science

About the role

• Job Posting Title: Postdoctoral Fellow, Department of Computer Science
• Hiring Department: Department of Computer Science
• Position Open To: All Applicants
• Weekly Scheduled Hours: 40
• FLSA Status: Exempt
• Earliest Start Date: Immediately
• Position Duration: Expected to Continue Until Dec 31, 2027
• Location: AUSTIN, TX

Job Details

General Notes

Project Affiliation: Federally sponsored research in predictive intelligent networking

Position expected to continue until March 1, 2027.

Must be eligible to work in the United States on a full time basis for any employer.

Purpose

Engineer and productionize components of PIN agents and the supporting training/experiment stack: scalable simulation/emulation harnesses, on-node runtimes, telemetry, and CI/CD. You'll collaborate with researchers implementing multi-agent RL, robust decision-making under uncertainty, and contingency-aware multi-path planning for mobile networks.

What We Offer

• A competitive post-doctoral salary and comprehensive benefits.
• Access to leading-edge technology and research facilities.
• A collaborative environment that fosters growth and innovation.
• Involvement in a project with tangible impact on the future of C5ISR systems.

Responsibilities

• Implement and harden agent/runtime services (Python + C++/Rust), data models, and APIs (gRPC/REST); package for edge compute deployments.
• Build and maintain simulation->emulation->HIL pipelines (e.g., ns-3/OMNeT++/CORE/EMANE); scenario generators; reproducible experiment harnesses.
• Integrate training loops (PyTorch/JAX), experiment orchestration, metrics/telemetry (Prometheus/Grafana-class).
• Own CI/CD, testing, and documentation; contribute to technical reports.

Required Qualifications

• A Ph.D. in Computer Science, AI, Networking, or a related discipline, earned within the last three years.
• Solid experience with AI/machine learning methodologies, particularly those applicable to network optimization.
• Proven ability in programming and familiarity with network simulation tools and environments.
• A strong propensity for innovative thinking coupled with a disciplined approach to research and collaboration.

Preferred Qualifications

• Publications or significant contributions to the field of AI, machine learning, or networking.
• Experience with interdisciplinary research and collaborative projects.
• Familiarity with military or defense communication systems is a plus.

Working Conditions

• Standard office environment

Required Materials

• Resume/CV
• Letter of Interest with research statement
• 3 reference letters
• Ph.D. verification

Additional information

Important for applicants who are NOT current university employees or contingent workers: You will be prompted to submit your resume the first time you apply, then you will be provided an option to upload a new Resume for subsequent applications. Any additional Required Materials (letter of interest, references, etc.) will be uploaded in the Application Questions section; you will be able to multi-select additional files. Before submitting your online job application, ensure that ALL Required Materials have been uploaded. Once your job application has been submitted, you cannot make changes.

Important for Current university employees and contingent workers: As a current university employee or contingent worker, you MUST apply within Workday by searching for Find UT Jobs. If you are a current University employee, log-in to Workday, navigate to your Worker Profile, click the Career link in the left hand navigation menu and then update the sections in your Professional Profile before you apply.

This information will be pulled in to your application. The application is one page and you will be prompted to upload your resume. In addition, you must respond to the application questions presented to upload any additional Required Materials (letter of interest, references, etc.) that were noted above.

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. This position has the option to elect the Optional Retirement Program (ORP) instead of TRS, subject to the position being 40 hours per week and at least 135 days in length.

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.