Informationen zum Job Weitere Informationen The successful candidate will be employed under a regular employment contract. The position is to be filled by 7/1/2025 and offered for a fixed term initially for 3 years. The fixed-term employment is possible as it constitutes one of the fixed-term options of the Wissenschaftszeitvertragsgesetz (German Act on Fixed-term Scientific Contracts). This is a part-time position (75 % of the standard weekly hours for full-time employees). The salary is based on the German public service salary scale (TV-L). The position corresponds to a pay grade of EG 13 TV-L. Unser Profil The Traffic Safety Group at the Institute for Highway Engineering (ISAC), RWTH Aachen University, focuses on improving road safety through research in traffic flow, road design, and accident analysis. Our interdisciplinary team integrates engineering and behavioural science to create innovative solutions for reducing accidents and increasing transportation efficiency. We maintain active collaborations with both national and international partners. The SEEclear project aims to develop an innovative software tool for effective speed monitoring in urban traffic. By using machine learning, location data, and advanced analytics, the software will provide data-based recommendations on where and how to control speed. This will help improve both traffic safety and environmental conditions. Ihr Profil • University degree (Master’s degree or equivalent) in transportation engineering, civil engineering, computer science, data science, or related fields. • Strong interest and foundational knowledge in traffic safety, data analysis, and/or machine learning. • Familiarity with statistical and machine learning techniques; prior experience with algorithms such as Random Forests, DBSCAN, or Decision Trees is beneficial. • Good programming skills in Python, R, or similar languages, and experience in data analysis software. • Knowledge of traffic simulation and geographic information systems (GIS) is advantageous. • Excellent English communication skills (written and spoken). • Fluency in German is advantageous. • Independent, proactive, problem-solving oriented, and comfortable working in interdisciplinary teams. Ihre Aufgaben • Conduct research in traffic safety, focusing on advanced data analysis, machine learning, and predictive modelling to identify optimal speed control strategies. • Develop, implement, and validate machine learning models for analysing traffic and safety data. • Prepare, process, and analyse diverse data sets (traffic measurements, accident statistics, infrastructure data, demographic information). • Conduct validations using collected local traffic data and historical data to ensure accuracy and reliability of developed models. • Collaborate closely with local municipalities, police departments, and industry partners to apply research outcomes in real-world scenarios. • Publish your findings in scientific journals and present your research at relevant conferences and workshops. • Support project management activities, including progress reporting and organisation of meetings. Über uns RWTH is a certified family-friendly University. We support our employees in maintaining a good work-life balance with a wide range of health, advising, and prevention services, for example university sports. Employees who are covered by collective bargaining agreements and civil servants have access to an extensive range of further training courses and the opportunity to purchase a job ticket. RWTH is an equal opportunities employer. We therefore welcome and encourage applications from all suitably qualified candidates, particularly from groups that are underrepresented at the University. All qualified applicants will receive consideration for employment and will not be discriminated against on the basis of national or ethnic origin, sex, sexual orientation, gender identity, religion, disability or age. RWTH is strongly committed to encouraging women in their careers. Female applicants are given preference if they are equally suitable, competent, and professionally qualified, unless a fellow candidate is favored for a specific reason. As RWTH is committed to equality of opportunity, we ask you not to include a photo in your application. You can find information on the personal data we collect from applicants in accordance with Articles 13 and 14 of the European Union's General Data Protection Regulation (GDPR) at http://www.rwth-aachen.de/dsgvo-information-bewerbung. Besoldung / Entgelt EG 13 TV-L