We seek one experienced member for our team, a Scientist or Engineer with a University Degree (Diploma) and a strong background in control systems, reinforcement learning, and autonomous robotics. The successful candidate will conduct research and development on intelligent control and planning algorithms for heterogeneous robotic formations within the framework of the SABER project. The SABER project (Strategic Autonomous vehicles for BattlEfield Reconnaissance and logistics), funded by the European Defence Fund (EDF), aims to develop a universal, multi-agent system of collaborative flying (UAV) and ground (UGV) robots. The project focuses on "Universal Multi-Agent Retrofitting," enabling existing platforms to perform complex aerial deliveries and data gathering in hostile, GNSS-denied, and unstructured environments. SABER researches hardware and software components as add-on solutions to enhance autonomous capacities for near real-time situational awareness. The successful candidate will be involved in the implementation of learning-based control frameworks and adaptive path planning strategies for multi-agent systems. Responsibilities include the development of Reinforcement Learning (RL) models for swarm coordination, the design of optimal control loops for UGV-UAV formations, and the preparation of technical documentation and simulation results. Furthermore, the candidate will support the integration of these intelligent algorithms into robotic simulation environments (digital twins) and contribute to the validation of autonomous navigation skills in accordance with the Grant Agreement.