PhD in Data-Driven Distributed Game Theory

Laplace Expert Ltd; Delft; Remote; EUR 36,000 - 45,000; Full time

Job summary

Delft University of Technology in Delft invites applications for a PhD candidate in data-driven distributed game theory within the ERC ARGON project. The position focuses on distributed and data-driven equilibrium seeking, multi-agent systems and optimization, with a 4-year full-time commitment.

The successful candidate holds a relevant MSc in systems and control, applied mathematics, electrical or mechanical engineering, and will develop theory and computational methods and contribute to

Qualifications

• MSc in systems and control or closely related field.
• Strong mathematical background and interest in optimisation.
• Background in game theory and multi-agent systems advantageous.

Skills

Game theory

Distributed optimization

Multi-agent systems

Convex optimization

Hybrid systems

Education

Systems & Control MSc

Applied Mathematics MSc

Electrical Engineering MSc

Mechanical Engineering MSc

Job description

PhD in Data-Driven Distributed Game Theory

Delft University of Technology · Delft Center for Systems and Control

Netherlands

Apply by 30 Oct 2026

About This Opportunity

Delft University of Technology is recruiting a PhD candidate in data-driven distributed game theory within the ERC-funded ARGON project. Applications close on 30 October 2026 at 23:59 Europe/Amsterdam.

The project develops theory and computational methods for dynamic games and multi-agent optimisation. Research focuses on distributed and data-driven equilibrium seeking, hybrid systems, convex-monotone game formulations and open-source algorithmic tools.

Details

game theory distributed optimization multi-agent systems control ARGON

Level PhD

Time Commitment Full-time

Duration 4 years

Reference Code 3862

Field Distributed Game Theory and Multi-Agent Control

Specialization Mathematics

Organization Type University

Funding & Compensation

Amount EUR 3204-4051 / Per Month

Funding Type Salary

Funding Status Paid

Period Per Month

Requirements

Education

Applicants should hold a relevant MSc in systems and control, applied mathematics, electrical engineering, mechanical engineering or a closely related field.

Experience

A strong mathematical background and interest in optimisation, game theory, multi-agent systems or nonlinear control is relevant.

Technical Skills

• Game theory
• Distributed optimization
• Multi-agent systems
• Convex optimization
• Hybrid systems