Research Intern: Coordination in Multi-Agent Systems

Honda Ri; San Jose (CA); Presencial; USD 36.000 - 54.000; Jornada completa

Descripción de la vacante

Honda Research Institute USA in San Jose, CA is offering a research internship focused on distributional foundations of multi-agent collaboration. You will develop mathematical models and algorithms to quantify coordination benefits in shared tasks, with emphasis on optimal transport and distribution-based agent behavior.

The role blends theory and computation: you will implement Python-based experiments, analyze results, and contribute to publications. Ideal candidates are pursuing a Ph.D.

Formación

• Currently pursuing a Ph.D. in a relevant field.
• Strong mathematical background including probability, linear algebra and optimization.
• Experience formulating and analyzing mathematical or computational models.

Responsabilidades

• Develop and analyze mathematical models for quantifying the benefit of coordination in multi-agent systems.
• Apply optimal transport and related optimization methods to joint distributions over agent behaviours.
• Investigate how the benefits of coordination depend on task structure, agent behavior models & interaction geometry.
• Develop theoretical results and computational experiments characterizing properties of coordination metrics.
• Implement and evaluate algorithms in Python using numerical optimization and scientific computing tools.
• Design simulation experiments to validate theoretical predictions and extensions to human-robot settings.
• Contribute to research publications and presentations resulting from the work.

Conocimientos

Mathematics

Probability

Linear algebra

Optimization

Python

Educación

Ph.D. in Computer Science, Robotics, Applied Mathematics, Electrical Engineering, Statistics, Operations Research or closely related field

Herramientas

Python

SciPy/NumPy

Descripción del empleo

Honda Research Institute USA in San Jose, CA is offering a research internship focused on distributional foundations of multi-agent collaboration. You will develop mathematical models and algorithms to quantify coordination benefits in shared tasks, with emphasis on optimal transport and distribution-based agent behavior.

The role blends theory and computation: you will implement Python-based experiments, analyze results, and contribute to publications. Ideal candidates are pursuing a Ph.D.