Doctoral Student in Reinforcement Learning for Agent-Based Models of Travel Behaviour KTH Royal Institute of Technology in Sweden

Project Overview: This doctoral position at KTH Royal Institute of Technology focuses on developing a dynamic agent-based model to investigate travel behavior change, grounded in the Theory of Planned Behaviour. The research aims to understand how repeated behavioral choices influence individual attitudes over time, using a feedback mechanism enhanced by soft interventions such as subsidies, rewards, nudging, and gamification. The ultimate goal is to encourage sustainable travel choices and model how individual decisions aggregate into system-wide travel demand patterns.

Research Focus: The project leverages reinforcement learning and agent-based modeling to simulate intelligent agents operating under bounded rationality. These agents adapt their behavior based on rules or learning, employing strategies like utility maximization and regret minimization. The simulation explores the interplay between psychological determinants (attitudes, subjective norms, perceived behavioral control) and structural incentives, providing insights into evolving behavioral patterns in transport systems.

Supervision and Environment: The doctoral student will be supervised by Dr. Fariya Sharmeen and will join a creative, dynamic, and international research environment at KTH in Stockholm, Sweden. KTH is a leading technical university known for its commitment to sustainability, innovation, and high-quality research. The position offers excellent working conditions, employee benefits, and opportunities for professional growth.

Funding and Employment: The position is a full-time, temporary employment for up to four years, with a monthly salary according to KTH's doctoral student salary agreement. The role includes access to workplace resources and employee benefits. Employment may be renewed annually, with a maximum duration corresponding to full-time doctoral education.

Eligibility and Requirements: Applicants must hold a second cycle degree (e.g., master's) or equivalent, or have completed at least 240 higher education credits (with at least 60 at the second-cycle level). English proficiency equivalent to English B/6 is required. Candidates should have a background in applied mathematics, transport science, computational science, machine learning, or AI, and possess strong programming skills. Experience with theory-driven models, research question formulation, data collection (including social media mining), and teamwork in multidisciplinary settings is essential. Additional desirable qualifications include data science expertise, integrated modeling, and critical thinking.

Application Process: Applications must be submitted through KTH's recruitment system. Required documents include certified copies of diplomas and grades, proof of language proficiency, a CV, an application letter (max 2 pages), and representative publications or technical reports. The application deadline is April 16, 2026.

Contact: For further information, contact Dr. Fariya Sharmeen at [email protected].

Join KTH and contribute to cutting-edge research in sustainable transport and intelligent systems within a supportive and innovative academic community.