Research Associate, Autonomous Multi-Agent Systems

Louisiana State University; Baton Rouge (LA); On-site; USD 65,000 - 95,000; Full time

Job summary

Louisiana State University seeks a Research Associate 5 to lead research on decision-driven information distillation for autonomous multi-agent systems. The role focuses on developing estimation, memory, and communication methods under partial observability and constrained resources, with reproducible software and simulation-based demonstrators.

The candidate will integrate filtering, belief estimation, and decision interfaces within a computational framework, collaborate with faculty and

Qualifications

• Master's degree required in a relevant engineering/computing field.
• Ph.D in a related field may substitute for required experience.
• 2 years of relevant research experience and demonstrated research productivity.

Responsibilities

• Conduct research on autonomous multi-agent systems with partial observability and limited communication.
• Develop methods for observation retention, belief updates, and information sharing.
• Implement methods in reproducible software framework linked to a downstream planner.
• Build and extend multi-agent object-search environments modeled as POMDP/Dec-POMDP.
• Collaborate with faculty and students to plan simulations and document results.

Skills

Strong programming skills

Experience with Python and ML tools

Education

Master's degree in electrical engineering, robotics, computer engineering, mechanical engineering, systems and control, computer science, applied mathematics, computational mathematics

Ph.D in related field (preferred)

Tools

Python

PyTorch

JAX

TensorFlow

C++

Job description

Louisiana State University seeks a Research Associate 5 to lead research on decision-driven information distillation for autonomous multi-agent systems. The role focuses on developing estimation, memory, and communication methods under partial observability and constrained resources, with reproducible software and simulation-based demonstrators.

The candidate will integrate filtering, belief estimation, and decision interfaces within a computational framework, collaborate with faculty and