I-2414 – PHD CANDIDATE IN DRIVER DIGITAL TWIN FOR THE TIRE INDUSTRY

How will you contribute?

The evaluation by test drivers of new tire designs in realistic simulations is a key part of the tire development process. In this context the role of the tester and the feedback it gives about the car and tires behaviour is determinant for the tire engineers to build perfect tires. Today, because it involves specialised testers, the process is time consuming and limited to the feedback of the available tester groups. With the rise of Artificial Intelligence and deep data analytics, there is an opportunity to complete the human crew by an artificial crew, to get more valuable feedback that can be integrated in the shaping of future tires.

You address the challenge of building a Driver Digital Twin (DDT) model of the human test drivers that are required to evaluate the effect of a tire on subjective handling performances in simulated environments, including the latest state-of-the-art immersive car simulation platform which is used by Goodyear in Luxembourg and the USA. While it aims to provide a mean to automate manoeuvres and feedback generation with a human digital twin, it will provide at the same time a better understanding of the human factors acting in subjective handling and evaluation. Classical approaches to human driver behaviour simulation introduce human factors in a control loop. The research here will go further by exploring hybrid artificial intelligence based on mixes of symbolic (knowledge representation and reasoning) and sub-symbolic data-based approaches (artificial neural networks, deep learning), and will seek to build a driver digital twin as an artificial agent capable of explaining its behaviour. Prototypes developed will be assessed by comparing predictions with subjective handling performed by human drivers across driving behaviour, subjective evaluation, and explanations.

Activities

• Conduct extensive background literature analysis, including works in both computer science and automated control,
• Design the DDT model and supporting algorithms, elaborate validation use-cases and scenarios, participate to the planning, organisation of the driver studies under simulator conditions, their analysis and the integration of gathered data to feed AI models,
• Presentation of papers at academic conferences,
• Writing of research papers and publication of peer-reviewed journal articles,
• Write a PhD thesis in the field of computer engineering,
• Take part in the PhD and research training,
• Participation to outreach activities of LIST.
• Collect all the information involved in the evaluation of the security posture (centralization of logs, administration of the SIEM solution)

Are you passionate about research? So are we! Come and join us

The Luxembourg Institute of Science and Technology (LIST) is a Research and Technology Organization (RTO) active in the fields of materials, environment and IT. By transforming scientific knowledge into technologies, smart data and tools, LIST empowers citizens in their choices, public authorities in their decisions and businesses in their strategies.

In 2024, LIST and GOODYEAR signed the second phase of their partnership, 2024-2029, building on the outcomes of their previous collaboration and entering new technological areas as well. GOODYEAR-LIST partnership 2.0 embraces Luxembourg's National priorities, such as Sustainability, Digital transformation and Circular economy, through the execution of six Strategic Research Programs: Data Science for Tires, Tire as a Sensor, End-of-Life Tire Valorization, Sustainable Materials for Non-Pneumatic Tires, Sustainable Materials for Next Generation of Pneumatic Tires, Structure-Process-Properties Relationships. As part of our Data Science for Tires strategic research program, we are looking for a PhD candidate in artificial intelligence and control to conceive a driver digital twin.

Do you want to know more about LIST? Check our website: https://www.list.lu/

Is Your profile described below? Are you our future colleague? Apply now!

Education

• A Master’s degree or Engineer diploma in artificial intelligence, automated control or engineering science, cognitive science, or data science.

Experience and skills

• Demonstrated knowledge and skills in both symbolic and sub-symbolic Artificial Intelligence methods (KRR, ANN and Deep Learning, XAI…),
• Demonstrated knowledge and skills in automated control and the integration of human aspects in the control loop, practical knowledge of Simulink and Matlab,
• Strong programming skills, in particular with Python. Proven experience with machine learning and data science libraries (Tensorflow, Keras, PyTorch, scikit-learn, numpy, pandas, ...). Skills in C/C++/C# or Java is an asset.
• Good understanding of user modelling or the modelling of human factors, in particular cognitive aspect.

Language skills

• Proficiency in written and spoken English and good level written and spoken. Knowledge of French is an asset but not mandatory.