Postdoctoral Fellow
Postdoctoral Fellow
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Job Description
Job Posting Title:
Postdoctoral FellowHiring Department:
Department of Computer SciencePosition Open To:
All ApplicantsWeekly Scheduled Hours:
40FLSA Status:
ExemptEarliest Start Date:
Jun 01 2024Position Duration:
Expected to Continue Until Mar 01 2027Location:
AUSTIN TXJob Details:
General Notes
Must be eligible to work in the United States on a fulltime 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 AIDriven Network Optimization
About the Project:This exciting opportunity at the University of Texas at Austin involves working on a cuttingedge 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 decisionmaking 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 machinelearning algorithms specifically designed for network optimization and security challenges. Through rigorous realworld 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:
A dynamic and collaborative research environment at the University of Texas at Austin.
Opportunities to work on pioneering technologies in AI and network security.
Access to stateoftheart 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.
100% employerpaid basic medical coverage
Retirement contributions
Paid vacation and sick time
Paid holidays
Please visit ourHuman Resources (HR) websiteto learn more about the total benefits offered.
Responsibilities
Collaborate in the conceptualization and development of theoretical frameworks to underpin AIdriven network optimization.
Engage in the design and iterative refinement of AI agents with a special focus on traffic prioritization and network adaptability.
Play a pivotal role in controlled scenario testing contributing to rigorous result analysis and validation.
Support the research team by assisting in the preparation of detailed technical reports and presentations that demonstrate project milestones and insights.
Required Qualifications
Ph.D. in Computer Science AI Networking or a related discipline within the last 3 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.
Salary Range
$70000 depending on qualifications
Working Conditions
Standard office conditions
Required Materials
Letter of Interest
Research Statement
Resume/CV
- Arrange at least three (3) confidential reference letters be sent to
Proof of Ph.D. in Computer Science AI Networking or a related discipline earned within the last three years.
Importantfor 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 multiselect additional files. Before submitting your online job application ensure thatALLRequired 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 login 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 questionspresented to upload any additional Required Materials (letter of interest references etc.) that were noted above.
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.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.Background Checks:
A criminal history background check will be required for finalist(s) under consideration for this position.
Equal Opportunity Employer:
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 contractors legal duty to furnish information.
Employment Eligibility Verification:
If hired you will be required to complete the federal Employment Eligibility Verification I9 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.
EVerify:
The University of Texas at Austin use EVerify to check the work authorization of all new hires effective May 2015. The universitys company ID number for purposes of EVerify is 854197. For more information about EVerify please see the following:
- EVerify Poster (English) PDF
- EVerify Poster (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). 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 HOP3031.
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.
Employment Type
Full-Time
