Research Intern: Mathematical Foundations of Multi-Agent Task Collaboration

Honda Research Institute USA; San Jose (CA); Sur place; USD 28 000 - 41 000; Temps partiel

Résumé du poste

Honda Research Institute USA in San Jose, CA invites a research intern to study the mathematical foundations of human-robot collaboration, with emphasis on optimal transport and distribution-based models of agent behavior.

The role combines mathematical analysis, algorithm development, and computational experiments to understand coordination in human-robot and multi-agent systems. Strong Python skills and a Ph.D. focus are required.

Qualifications

• Ph.D. candidate in Computer Science, Robotics, Applied Mathematics, Electrical Engineering, Statistics, OR related field.

Responsabilités

• Develop and analyze mathematical models for coordination benefits in multi-agent systems.
• Apply optimal transport to joint distributions over agent behaviors.
• Investigate how task structure affects coordination and interaction geometry.
• Develop theoretical results and computational experiments for coordination metrics.
• Implement algorithms in Python using numerical optimization and scientific computing tools.
• Design simulation experiments to validate predictions and extend to human-robot settings.
• Contribute to research publications and presentations.

Connaissances

Python

Mathematics

Optimization

Probability

Independent research

Formation

Ph.D. student or Ph.D. candidate in a related field

Outils

CVXPY

SciPy

Description du poste

Research Intern: Mathematical Foundations of Multi-Agent Task Collaboration - Honda Research Institute USA

Research Intern: Mathematical Foundations of Multi-Agent Task Collaboration

Job Number: P25INT-61

Honda Research Institute USA (HRI-US) is seeking a Honda Research Institute USA (HRI-US) is seeking a research intern to investigate the mathematical foundations of human-robot collaboration. The intern will develop methods for quantifying when and how much multi-agent systems can benefit from coordination, with a particular focus on optimal transport and distribution-based models of agent behavior. The work will combine mathematical analysis, algorithm development, and computational experiments to study fundamental properties of coordination in human-robot and multi-agent systems.

San Jose, CA

Key Responsibilities

• 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 behaviors.
• Investigate how coordination benefit depends on task structure, agent behavior models, and 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 explore extensions to human-robot and multi-agent settings.
• Contribute to research publications and presentations resulting from the work.

Minimum Qualifications

• Currently pursuing a Ph.D. in Computer Science, Robotics, Applied Mathematics, Electrical Engineering, Statistics, Operations Research, or a closely related field.
• Strong mathematical background, including probability, linear algebra, and optimization.
• Experience formulating and analyzing mathematical or computational models.
• Proficiency in Python and scientific computing.
• Ability to independently develop, implement, and evaluate research ideas.

Bonus Qualifications

• Experience with optimal transport, convex optimization, or distributional optimization.
• Background in probability theory, information theory, statistical learning, or mathematical statistics.
• Experience with multi-agent systems, robotics, human-robot interaction, or decision-making.
• Familiarity with numerical optimization packages such as CVXPY, SciPy, or related tools.
• Experience developing theoretical results alongside computational experiments.
• Strong research publication record in machine learning, robotics, optimization, applied mathematics, or related areas.

Years of Work Experience Required 0

Desired Start Date 1/11/2027

Internship Duration 3 Months

Position Keywords Optimal transport; human-robot collaboration; multi-agent systems; coordination; coordination benefit; probability distributions; convex optimization; distributional optimization; information theory; mathematical modeling; decision-making; robotics; machine learning; statistical dependence