Multi-Agent Reinforcement Learning Research Scientist

Pheratech Systems is seeking a Research Scientist to develop new methods for learning, prediction, and decision-making in coordinated robotic systems. The role focuses on model-based reinforcement learning, multi-agent systems, world modeling, state estimation, planning under uncertainty, and the integration of compact vision-language models with robotic behavior.

Responsibilities:

• Originate research questions and develop novel algorithms, representations, learning objectives, or theoretical insights
• Develop world models and model-based RL methods for interacting, heterogeneous agents
• Investigate how communication, partial observability, changing team composition, and coupled dynamics affect prediction and control
• Develop uncertainty-aware exploration and planning methods that use limited interaction data effectively
• Investigate hierarchical and temporal representations, including skill discovery, memory, and planning across multiple timescales
• Adapt small VLMs and connect multimodal representations to world models, planners, and robotic behavior
• Design statistically rigorous experiments that distinguish competing explanations, test assumptions, and establish the limits of a method
• Collaborate with researchers and simulation engineers to connect scientific advances with robotic coordination
• Lead research projects through implementation, validation, technical writing, and publication where appropriate
• Attend conferences and workshops and assist with writing proposals