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Zhenliang Ma

Top university

7 months ago

Doctoral Student in Agentic LLM for Transport Planning KTH Royal Institute of Technology in Sweden

Degree Level

PhD

Field of study

Computer Science

Funding

Available

Deadline

Expired

Country flag

Country

Sweden

University

KTH Royal Institute of Technology

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Keywords

Computer Science
Artificial Intelligence
Civil Engineering
Python Programming
Monte Carlo Simulation
Environmental Sustainability
Resiliency
Large Language Models
Tranport Planning

About this position

Project Overview: The Doctoral Student in Agentic LLM for Transport Planning at KTH Royal Institute of Technology offers an exciting opportunity to address critical challenges in transport planning under deep uncertainties at both national and international levels. The project is situated within the third-cycle subject of Transportation Science and responds to evolving demands such as the EU Green Deal, resilience missions, geopolitical shifts, technological disruptions, and sustainability targets. Traditional transport models, including 4-step demand models, simulations, and agent-based models, often struggle to adapt to rapidly changing conditions. This project aims to leverage large language models (LLMs) to dynamically adapt inputs, projections, and prediction mechanisms, thereby enhancing the resilience and robustness of transport planning.

Research Environment: The successful candidate will work collaboratively at KTH in Sweden and DTU in Denmark, developing LLM-driven models for decision-making under deep uncertainties. The position is supervised by Associate Professor Zhenliang Ma, an expert in the field. The research will focus on integrating AI and advanced computational methods into transport modeling, with a strong emphasis on programming (Python), resilience, and sustainability.

Eligibility and Requirements: Applicants must hold a second cycle degree (such as a master's degree) or have completed at least 240 higher education credits (with at least 60 at the second-cycle level), or possess equivalent knowledge. Strong research experience in transport modeling, AI/LLM, and technical skills in Python programming are essential. Proficiency in English equivalent to English B/6 is mandatory. Candidates should demonstrate goal orientation, perseverance, independence, collaboration skills, and the ability to analyze complex issues. Selection will emphasize both qualifications and personal skills.

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 employee benefits and the possibility to perform certain additional tasks (up to 20%). The position is based in Stockholm, Sweden, with opportunities for international collaboration.

Application Process: Applications must be submitted through KTH's recruitment system by April 2, 2026. Required documents include a CV, application letter (max 2 pages), diplomas and grades, language certificates, and representative publications or technical reports. All documents not in English or Swedish must be accompanied by certified translations. Only complete applications received by the deadline will be considered.

About KTH: KTH Royal Institute of Technology is a leading international technical university, committed to education, research, and innovation for a sustainable society. The university offers a creative and dynamic environment, attractive benefits, and a strong commitment to equality, diversity, and equal opportunities.

For further information, contact Associate Professor Zhenliang Ma at [email protected].

Funding details

Available

What's required

Applicants must have a second cycle degree (e.g., master's degree) or have completed at least 240 higher education credits, including at least 60 at the second-cycle level, or possess equivalent knowledge. Strong research experience in transport modeling, AI/LLM, and technical skills in Python programming are required. English proficiency equivalent to English B/6 is mandatory. Candidates should be goal-oriented, persevering, able to work independently and collaboratively, and capable of analyzing complex issues. Application must include CV, application letter, diplomas and grades, language certificates, and representative publications or technical reports.

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Professors

Associate Professor at KTH Royal Institute of Technology

University Name
Country flag

Sweden

Research Interests

Transportation Engineering

Automotive Engineering

Multimodal Transportation

Traffic Management

Travel Behavior