Sd-25009 Postdoc On Machine Learning For Road Characterization

Temporary contract | 24 months | Belval

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.

How will you contribute?

Do you have proven skills in data analysis, machine learning, as well as in mathematical and computational modelling?

You will have the opportunity to investigate innovative solutions using machine learning algorithms and predictive modelling techniques in the context of a collaborative project with Goodyear Luxembourg (one of our national industrial partners).

Road condition is an essential part of mobility which influences virtually all facets of driving and tire performances. That is why it is becoming a demanding strategic research area that significantly impacts the tire development industry. Mainly road conditions are characterized by the friction coefficient. However, it can be extended to study the granularity of the road surface, roughness, and holes. The prediction of the road conditions is linked to several external variables related to the weather conditions, road surface micro- and macro-texture, tire properties, driving speed, and behaviours of road users. This project aims at studying different data sources such as TPMS data, high-speed wheel encoders, CAN data, accelerometer data, and acoustics data. In addition to other cloud-based data for weather-friction estimates and crowdsourced vehicle data for estimating road conditions.

Your specific activities will include (but are not limited to):

• Develop robust, production-grade machine learning solutions for predictive modelling and complex decision-support systems.

• Develop scalable and efficient ML pipelines using MLOps best practices.

• Address challenges related to real-world sensor data, such as faulty or missing data.

• Develop and implement techniques for data validation and consistency checks using real-world industrial datasets.

• Disseminate the project results through project reports and deliverables, scientific publications in journals and conferences and presentations at industry events.

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

Education:

• PhD degree on Computer Science and Engineering, Telecommunication Engineering, Applied Mathematics or Statistics with application or specialization in Data Science.

Required experience and skills:

• Extensive experience in machine learning methods, tools, and platforms.

• Proficiency in Python, with demonstrated software development experience.

• Hands-on experience in MLOps, including the design and deployment of production-grade ML pipelines.

• Solid mathematical foundation, with knowledge of statistical analysis.

• Experience in using database systems (relational, data warehouse and data lake).

• Experience in research, including designing studies, formulating research questions, and applying appropriate methodologies.

• Sound publication track record in relevant international conferences and journals.

• Ability to present research findings in a clear and concise manner through reports, presentations, and scientific papers.

• Experience of working in applied research projects preferably with industrial partners.

• Ability to work in teams geographically located in different partner’s facilities.

• Strong interpersonal and communication skills to collaborate effectively with cross-functional teams, stakeholders, and industry experts.

• Good communication skills.

• Good written and verbal communication skills in English is mandatory.

Nice to have experience:

• Statistical knowledge: Experience in uncertainty analysis, particularly in the context of large datasets and real-world statistical modelling.

• Road Condition Research: Knowledge and experience in the field of road condition research, including an understanding of road surface characteristics, friction coefficient, road texture, roughness, and related parameters. Familiarity with relevant research methodologies and techniques.

• Data Collection and Sensors: Familiarity with different data collection methods and sensors used to gather road condition data, including TPMS (Tire Pressure Monitoring System), high-speed wheel encoders, CAN (Controller Area Network) data, accelerometers, and acoustics data. Understanding of sensor technologies and their limitations.

• Experience in managing research projects, including planning, organizing, and coordinating research activities within specified timelines. Ability to prioritize tasks, set milestones, and monitor progress.

• Continuous Learning and improvement: Willingness to stay updated with the latest advancements in road condition research, tire technology, data analysis techniques, and industry trends. Actively seeking model improvements based on the industry changes.

Your LIST benefits

• 
  An organization with a passion for impact and strong RDI partnerships in Luxembourg and Europe that works on responsible and independent research projects

• Sustainable by design, empowering our belief that we play an essential role in paving the way to a green society

• Innovative infrastructures and exceptional labs occupying more than 5,000 square metres, including innovations in all that we do

• An environment encouraging curiosity, innovation and entrepreneurship in all areas

• Personalized learning programme to foster our staff’s soft and technical skills

• Multicultural and international work environment with more than 50 nationalities represented in our workforce

• Diverse and inclusive work environment empowering our people to fulfil their personal and professional ambitions

• Gender-friendly environment with multiple actions to attract, develop and retain women in science

• 32 days’ paid annual leave, 11 public holidays, 13-month salary, statutory health insurance

• Flexible working hours, home working policy and access to lunch vouchers

Apply online

Your application must include:

• A motivation letter oriented towards the position and detailing your experience

• A scientific CV with contact details

• List of publications (and patents, if applicable)

• Contact details of 2 references

Please apply ONLINE formally through the HR system. Applications by email will not be considered.

Application procedure and conditions

• We kindly request applicants to provide their nationality for statistical purposes only, as part of our commitment to promoting diversity and ensuring equal opportunities in our workforce. This information will be kept confidential and will not be used for any discriminatory purposes.

• LIST is dedicated to maintaining an inclusive work environment and is an equal opportunity employer. We are committed to attracting, hiring, and retaining a diverse workforce. All applicants will be considered for employment without discrimination based on national origin, race, colour, gender, sexual orientation, gender identity, marital status, religion, age, or disability.

• Applications will be continuously reviewed until the position is filled. An assessment committee will thoroughly evaluate applications, adhering to guidelines designed to ensure equal opportunities. The primary criteria for selection will be the alignment of the applicant's existing skills and expertise with the requirements mentioned above.

• Please note that by applying, you consent to share your application with Mrs Veronique Martin-Lang, Chief Engineer Data Science and Analytics, and Mrs. Eman Ahmed and Mrs Maja Adzaga, Data Scientists, all three from Goodyear Luxembourg.