Fuel consumption of vehicles depends on several factors, including rolling resistance (RR). RR is defined as the energy lost when the vehicle tyre rolls over a pavement surface, and to a great extent is determined by three pavement-vehicle interaction (PVI) mechanisms: pavement roughness, texture and deflection.

Although most of the strategies adopted by the highway agencies to reduce the environmental footprint of their pavement construction and maintenance practices consists of using waste and recycled materials, low temperature asphalt mixtures, and in-place recycling techniques, a more meaningful reduction can be achieved by lowering the environmental impacts arising from the movement of vehicles due to the pavement condition and characteristics.

To help highway agencies to achieve their long-term sustainability goals, a software is needed that enable them to quickly quantify the impacts of pavement condition and characteristics on RR and consequent effects on additional vehicle fuel consumption.

The main objective of the project:

To develop a software that deploys data driven models intended to estimate the effects of pavement condition and characteristics on RR and consequent additional vehicle fuel consumption under driving conditions. The goal is to improve the understanding and modelling of the role of road pavements on additional vehicle fuel consumption due to RR. Data collection campaigns will be performed by using an instrumented vehicle. The obtained data will be analysed using data mining techniques to obtain data-driven knowledge and develop models that capture the complex correlations between the variables involved in the PVI mechanisms and their effects on vehicle fuel consumption.

The expected outcomes of the project:

• Data-driven models to estimate the PVI effects on additional vehicle fuel consumption.
• A software that deploys the developed models to allow the quick assessment of the impact of pavement design and management decisions on the vehicle energy consumption and environmental impacts arising from the pavement use phase.

We are looking for the best MSc graduated with a background on civil engineering, mechanical engineering, systems & control or automation field. Besides, the candidate must be eager to learn and have a can-do mindset and the ambition and talent to find solutions/craft designs for complex technological issues with a multidisciplinary character, strong problem solving abilities and out-of-the-box thinking. Strong knowledge and hands-on experience with data analytics, handling and processing big data, machine learning algorithms, statistics and programming languages (preferentially Python) are of upmost importance for the candidate to successfully complete the project. Communication and interpersonal skills, teamwork, self-initiative, self-motivation and capacity to deliver accurate work are equally appreciated. Finally, professional command of English (both written and spoken) is mandatory.

We offer you a very challenging position in an inspiring multidisciplinary environment. In this time we will provide you with a tailor made post-master design program that has an educational component (~50%) as well as a design project (~50%). The project will be performed in collaboration with the Gustave Eiffel University's EASE laboratory.

We offer:

• A salary of € 1.970 gross per month.
• An annual holiday allowance of 8% of the gross annual salary, and an annual year-end bonus of 8.3%.
• A solid pension scheme.
• Minimum of 29 leave days in case of full-time employment.

On successfully completing the program, you will receive a certified and recognised degree. You will be entitled to use the academic degree PDEng and will be registered as a Technological Designer in the Dutch register kept by the Royal Institution of Engineers in the Netherlands (KIVI).