Research Intern: Mathematical Foundations of Multi-Agent Task Collaboration
Listed on 2026-09-13
-
Research/Development
Mathematics, Data Scientist, Research Scientist, Operations Research Analyst
Research Intern:
Mathematical Foundations of Multi-Agent Task Collaboration - Honda Research Institute USA Research Intern:
Mathematical Foundations of Multi-Agent Task Collaboration
Job Number: P25IN
T-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, Sci Py, 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
(If this job is in fact in your jurisdiction, then you may be using a Proxy or VPN to access this site, and to progress further, you should change your connectivity to another mobile device or PC).