Open PhD Position - Autonomous Transport for Harmonised Low-Carbon Operations and Sustainability (ATHLOS) project (TSE 124)

Technical University of Munich – Imperial College London (TUM-Imperial) Joint Academy of Doctoral Studies

Description of the Chair

The Chair of Transportation Systems Engineering (TSE) undertakes research in the transportation field with a specific focus on the modelling and simulation of transportation systems, implementation of data science and data analytics in transport and human factors analysis, and machine learning and deep learning. The TSE chair performs research on both multimodal and unimodal freight and passenger transport demand and supply modelling, allowing for contributions on optimisation, calibration, and validation of transport models. In this direction, the application of Big Data acquisition and analysis is examined as well as the use of Data-driven flexible models. The TSE chair also contributes to the analysis of human factors in transport-related fields such as road safety modelling, behavioural economics applications, and modelling of factors that affect transportation systems user engagement.

Project Description

The transport sector is a key contributor to environmental pollution and energy consumption globally. Urban road transport is widely regarded as the most polluting transport mode, with negative externalities arising from the high density of traffic and consistent stop-and-go operational patterns in chronically congested urban road networks. The aim of the ATHLOS project is to develop feasible interventions to quantify and minimise emissions and improve safety on urban transport networks, specifically, through the application of advanced traffic management techniques and the newest innovations in Connected and Autonomous Vehicle (CAV) technologies. The project involves two main objectives: (i) to quantify baseline transport emissions and energy use patterns in urban road networks, and (ii) to develop, evaluate, and recommend cooperative driving controllers with a specific focus on minimising transport emissions and fuel consumption, leveraging the latest advancements in connected and autonomous vehicle technologies. Munich and London will be used as case study cities in the project. 

The project will be delivered jointly alongside a PhD student at Imperial College London supervised by Dr. Panagiotis Angeloudis. The TUM team will be primarily responsible for scaling up and extrapolating the micro-level CAV control strategies modelling undertaken at Imperial College London. The TUM team will employ concepts from meso/macro-scopic traffic simulation modelling, network optimisation, and travel behaviour modelling to scale up the outputs of CAV-focused models on a city-wide scale, taking into account the complex dynamics of urban transportation systems. Based on the city-wide modelling outputs, we will identify potential challenges, opportunities, and policy implications associated with deploying advanced CAV technologies in the case study cities of Munich and London, to assess their potential impact on city-wide emissions, energy consumption, and overall traffic efficiency.

The PhD researcher will be supervised by Prof. Dr. Constantinos Antoniou  at TUM and cosupervised by Dr. Panagiotis Angeloudis from Imperial College London. As this is a collaborative research project between TUM and Imperial College London, the TUM PhD candidate will undertake a 10 month visit to Imperial College London and will participate in regular joint meetings and formal workshops both virtually and in-person.


• Have an MSc degree in a relevant field (e.g., transportation engineering, data science, computer science).

• Be enthusiastic about performing research on transport-related projects, and possess an understanding of the fundamentals of transportation systems and modelling.

• Have strong analytical skills – A strong knowledge of the principles of data science, statistical learning, simulation, and optimisation modelling will be important in developing and applying the quantitative methods that will be used to scale up the microscopic modelling to network level outputs. 

• Have excellent research, academic writing, and presentation skills.

• Like programming, and have an experience of using Python or R.

• Have excellent working knowledge (written and oral) of English (knowledge of German will be considered a plus); and

• Be able to work with strict deadlines.

In addition to the above, candidates must fulfil the general TUM admission requirements:

Conditions of Employment

The successful candidate will be funded by a maximum 4-year stipend from TUM IGSSE.


Interested applicants who fit the requirements of the position are asked to send the following to

• A curriculum vitae

• Academic transcripts

• A motivation letter

• The names and contact information of three references

Please include the position ID in the email subject ([TSE124] and your name). Review of applications will begin immediately and continue until the position is filled.