The PhD topic aims to address the issue of adequately quantifying cost of rolling resistance to the environment particularly under the Netherlands condition with the ultimate aim to reduce the carbon-footprint. Rolling resistance is directly linked to fuel consumption and hence carbon-footprint. The goal of the PhD topic in a way supports achieving the targets as defined in the Climate Treaty of Paris for creating a sustainable society. The PhD topic also focuses on monitoring of dedicated field sections with currently available facilities in parallel with developing a tool that aims to use the model-framework capable of explaining and giving deeper insight of rolling resistance's impact on environment. Moreover, classical data-analyses in combination with machine learning tools will be utilized as an interface to transfer the existing knowledge(database) to improve the environmental cost estimation of rolling resistance.

The knowledge of improved ECI could significantly impact the design and maintenance of asphalt pavements, life-cycle cost analysis and the development of low rolling resistant asphalt top layers. However, coming up with an accessible and robust ECI is a complex process since the rolling resistance is significantly affected by various factors related to traffic, road design, environment, vehicle class, tyre type and pavement condition. Although past research studies and projects developed statistics-based relationships, they are not directly applicable under the Netherlands road conditions. Therefore measurements were carried out on some national roads out in the recent past. These data can be a solid and sound base for deepening the knowledge on rolling resistance and fuel consumption. The candidate in his initial years is supposed to use the available data and data analysis techniques to identify key factors affecting Rolling resistance. In the later years,  the candidate is supposed to be involved in conducting field tests and utilize gained knowledge to develop a robust numerical tool which could help in getting state-of-the-art ECI for the Netherlands.

One of the research output is to develop an accessible and understandable tool to quantify the contribution of Rolling Resistance in the Environmental Cost Indicator (ECIRR) for different asphalt top layers. This tool will consider factors that can be directly affected by tyre properties, driving speed, wind, spinneys, baffle boards,   and also by pavement related factors such as International Roughness Index (IRI), mean profile depth, road design, bearing capacity, etc. This information should enable deeper understanding of the relations between rolling resistance and surface characteristics by monitoring dedicated field sections with currently available facilities in parallel with developing a tool that aims to use the model-framework capable of explaining and giving deeper insight of rolling resistance's impact on environment. Moreover, classical data-analyses in combination with machine learning tools will be utilized as an interface to transfer the existing knowledge (database) to improve the environmental cost estimation of rolling resistance. Furthermore, when deviations are seen in field performance, the gained knowledge will be used to identify the "cause and use" to define where more detailed knowledge is required.

Your PhD promotor will be Prof. Sandra Erkens. During your PhD period you are expected to interact with other (inter)national researchers and experts from Rijkswaterstaat and TNO.

• An MSc degree in the areas of Civil Engineering, Computational Materials Science, Applied Mathematics, Mechanical Engineering, Computer Science, or other related areas.
• Knowledge of basic material modelling, data analysis, continuum and discrete mathematical formulations, numerical skills and programming skills (Fortran / C++ / Python).
• Independent, self-motivated, eager to learn and open to communicate and collaborate with peers.
• Experience with advanced pavement material modeling, monitoring & field testing, data analysis, and machine learning/artificial intelligence is a plus.
• Good command of English language, both verbally and in writing. If your mother tongue is not English and you do not hold a degree from an institution in which English is the official language of instruction, you must submit proof of English proficiency from either TOEFL (minimum total score of 100) or IELTS (minimum total score of 7.0). Proof of English language proficiency certificates older than two years are not accepted.

Fixed-term contract: 4 years.

TU Delft offers PhD-candidates a 4-year contract, with an official go/no go progress assessment after one year. Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities, increasing from € 2395 per month in the first year to € 3061 in the fourth year. As a PhD candidate you will be enrolled in the TU Delft Graduate School. The TU Delft Graduate School provides an inspiring research environment with an excellent team of supervisors, academic staff and a mentor. The Doctoral Education Programme is aimed at developing your transferable, discipline-related and research skills.

The TU Delft offers a customisable compensation package, discounts on health insurance and sport memberships, and a monthly work costs contribution. Flexible work schedules can be arranged. For international applicants we offer the Coming to Delft Service and Partner Career Advice to assist you with your relocation.

Delft University of Technology

Delft University of Technology is built on strong foundations. As creators of the world-famous Dutch waterworks and pioneers in biotech, TU Delft is a top international university combining science, engineering and design. It delivers world class results in education, research and innovation to address challenges in the areas of energy, climate, mobility, health and digital society. For generations, our engineers have proven to be entrepreneurial problem-solvers, both in business and in a social context. At TU Delft we embrace diversity and aim to be as inclusive as possible (see our Code of Conduct). Together, we imagine, invent and create solutions using technology to have a positive impact on a global scale.

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Faculty Civil Engineering & Geosciences

The Faculty of Civil Engineering & Geosciences (CEG) is committed to outstanding international research and education in the field of civil engineering, applied earth sciences, traffic and transport, water technology, and delta technology. Our research feeds into our educational programmes and covers societal challenges such as climate change, energy transition, resource depletion, urbanisation and the availability of clean water, conducted  in close cooperation with a wide range of research institutions. CEG is convinced that Open Science helps to achieve our goals and supports its scientists in integrating Open Science in their research practice. The Faculty of CEG comprises 28 research groups in the following seven departments: Materials Mechanics Management & Design, Engineering Structures, Geoscience and Engineering, Geoscience and Remote Sensing, Transport & Planning, Hydraulic Engineering and Water Management.

Click here to go to the website of the Faculty of Civil Engineering & Geosciences.

You will be part of the Pavement Engineering section in the department of Engineering Structures at the CEG Faculty. Our section pursues state-of-the-art research in durable and sustainable bituminous materials and pavements, low-noise pavement surfaces, (epoxy-, rubber-, polymer-) modified pavements, (in-situ) rejuvenation technologies, material modeling, pavement monitoring and asphalt reuse and recycling. The education and research program of the Pavement Engineering section focuses on the characterization and monitoring of pavement materials at various scales, using both experiments and numerical models. The section is equipped with modern experimental and parallel computing facilities.