Research Associate – Decision‑Driven Information Distillation

About the role

This full‑time research position supports a federally funded project on decision‑driven information distillation for multi‑agent autonomy, focusing on how teams of autonomous agents filter, retain, represent, and share information under uncertainty.

Key responsibilities

• Conduct research on autonomous multi‑agent systems operating under partial observability, limited communication and constrained resources.
• Develop, implement and evaluate algorithms for information filtering, belief estimation, memory management and communication sharing in multi‑agent object‑search scenarios.
• Integrate filtering, estimation and decision‑interface components into a reproducible computational framework and coordinate simulation studies with faculty and graduate students.
• Prepare technical documentation, peer‑reviewed manuscripts, benchmark releases and regular project reports.

Required profile

• Master’s degree in a relevant engineering or computational field (Ph.D. acceptable) and at least two years of research experience.
• Experience with autonomous systems, robotics, estimation, control, machine‑learning or scientific computing.

Required skills

• Programming in Python and C++.
• Experience with deep‑learning frameworks such as PyTorch, JAX or TensorFlow.
• Knowledge of estimation, sensor fusion, data assimilation and control methods.
• Familiarity with multi‑agent decision processes (POMDP, Dec‑POMDP) and simulation environments.

What we offer

• Full‑time research position with a one‑year initial appointment, renewable based on performance and funding.
• Access to LSU’s health, dental, vision, life insurance, retirement plans, paid holidays and wellness benefits.
• Tuition exemption for qualified positions, professional development and training opportunities.
• Opportunity to publish research, release open‑source software and collaborate across engineering and mathematics departments.