The University of Texas at Austin
Postdoctoral Fellow
Job Description
Job Posting Title:
Postdoctoral Fellow
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Hiring Department:
Department of Computer Science
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Position Open To:
All Applicants
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Weekly Scheduled Hours:
40
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FLSA Status:
Exempt
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Earliest Start Date:
Jun 01, 2024
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Position Duration:
Expected to Continue Until Mar 01, 2027
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Location:
AUSTIN, TX
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Job Details:
General Notes
Must be eligible to work in the United States on a full-time basis for any employer. Position expected to continue until March 1, 2027.
Deadline for Application: Applications will be reviewed continuously until the position is filled.
Contact Information:
Must be eligible to work in the United States on a full-time basis for any employer. Position expected to continue until March 1, 2027.
Deadline for Application: Applications will be reviewed continuously until the position is filled.
Contact Information:
. Please note only applications through Workday will be considered.
Purpose
Project Affiliation: Army Contract for AI-Driven Network Optimization
About the Project: This exciting opportunity at the University of Texas at Austin involves working on a cutting-edge AI networking project under the guidance of Professor Chandrajit Bajaj. The project focuses on developing Predictive Intelligent Networking (PIN) agents, employing advanced AI techniques for rapid response decision-making in predictive intelligent communication networks. Our innovative approach centers on enhancing network efficiency, reducing overhead traffic, automating PACE communications planning, and improving scalability in challenging environments. Our project is dedicated to crafting advanced machine-learning algorithms specifically designed for network optimization and security challenges. Through rigorous real-world simulation scenarios, we aim to deliver robust solutions that excel in environments with incomplete or uncertain data. This role offers the chance to be part of a pioneering effort to create generic solutions for heterogeneous Army networks, working within the confines of existing network protocols.
Project Affiliation: Army Contract for AI-Driven Network Optimization
About the Project: This exciting opportunity at the University of Texas at Austin involves working on a cutting-edge AI networking project under the guidance of Professor Chandrajit Bajaj. The project focuses on developing Predictive Intelligent Networking (PIN) agents, employing advanced AI techniques for rapid response decision-making in predictive intelligent communication networks. Our innovative approach centers on enhancing network efficiency, reducing overhead traffic, automating PACE communications planning, and improving scalability in challenging environments. Our project is dedicated to crafting advanced machine-learning algorithms specifically designed for network optimization and security challenges. Through rigorous real-world simulation scenarios, we aim to deliver robust solutions that excel in environments with incomplete or uncertain data. This role offers the chance to be part of a pioneering effort to create generic solutions for heterogeneous Army networks, working within the confines of existing network protocols.
What We Offer:
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A dynamic and collaborative research environment at the University of Texas at Austin.
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Opportunities to work on pioneering technologies in AI and network security.
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Access to state-of-the-art facilities and resources at the Computer Visualization Lab.
- A chance to contribute to a project with a significant impact in the field of C5ISR communications.
UT Austin offers a competitive benefits package that includes:
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100% employer-paid basic medical coverage
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Retirement contributions
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Paid vacation and sick time
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Paid holidays
Please visit our
Human Resources (HR) website
to learn more about the total benefits offered.
Responsibilities
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Collaborate in the conceptualization and development of theoretical frameworks to underpin AI-driven network optimization.
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Engage in the design and iterative refinement of AI agents with a special focus on traffic prioritization and network adaptability.
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Play a pivotal role in controlled scenario testing, contributing to rigorous result analysis and validation.
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Support the research team by assisting in the preparation of detailed technical reports and presentations that demonstrate project milestones and insights.
Required Qualifications
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Ph.D. in Computer Science, AI, Networking, or a related discipline within the last 3 years
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Solid experience with AI/machine learning methodologies, particularly those applicable to network optimization.
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Proven ability in programming and familiarity with network simulation tools and environments.
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A strong propensity for innovative thinking coupled with a disciplined approach to research and collaboration.
Preferred Qualifications
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Publications or significant contributions to the field of AI, machine learning, or networking.
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Experience with interdisciplinary research and collaborative projects.
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Familiarity with military or defense communication systems is a plus.
Salary Range
$70,000 + depending on qualifications
Working Conditions
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Standard office conditions
Required Materials
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Letter of Interest
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Research Statement
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Resume/CV
- Arrange at least three (3) confidential reference letters be sent to
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Proof of Ph.D. in Computer Science, AI, Networking or a related discipline earned within the last three years.
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.
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Employment Eligibility:
Please make sure you meet all the required qualifications and you can perform all of the essential functions with or without a reasonable accommodation.
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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.
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Background Checks:
A criminal history background check will be required for finalist(s) under consideration for this position.
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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.
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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.
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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.
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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:
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E-Verify Poster (English)
[PDF]
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E-Verify Poster (Spanish)
[PDF]
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Right To Work Poster (English)
[PDF]
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Right To Work Poster (Spanish)
[PDF]
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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). If this position is identified a Campus Security Authority (Clery Act), you will be notified and provided resources for reporting. Responsible employees under Title IX are defined and outlined in
HOP-3031
.
The Clery Act requires all prospective employees 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.
