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.

Responsibilities

• Collaborate in the conceptualization and development of theoretical frameworks to underpin AI-driven 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, Computer Engineering, Computational Applied Mathematics, or a related discipline within the last 3 years
• Experience with statistical AI/machine learning methodologies, particularly those applicable to graph and network optimization.
• Proven ability in Python programming and familiarity with particle filters and graph neural 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

$70,000 + depending on qualifications

Required Materials

• Letter of Interest
• Research Statement
• Resume/CV
• Arrange at least three (3) confidential reference letters be sent to DBGapplications@cs.utexas.edu
• Proof of Ph.D. in Computer Science, AI, Networking or a related discipline earned within the last three years.

General Notes: 
Must be eligible to work in the United States on a full-time basis without sponsorship.  Position expected to continue until March 1, 2027.