Informationen zum Job Weitere Informationen The successful candidate will be employed under a regular employment contract. The position is to be filled at the earliest possible date and offered for a fixed term initially for 2 years. The option to extend the contract for three further years is planned. 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 full-time position. The successful candidate has the opportunity to pursue a doctoral degree in this position. 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 Institute of Control Engineering, directed by Prof. Heike Vallery, is part of the Faculty of Mechanical Engineering at RWTH Aachen University. The institute specializes in control and automation engineering and aims to act as a bridge between theory and application in the domains of control and automation engineering. In addition to offering fundamental education in control theory, the research work of the institute focuses on mechanical-, automotive-, process-, energy- and medical-engineering. For more information please see: www.irt.rwth-aachen.de Ihr Profil You have successfully completed a university degree (master’s degree or equivalent) in mechanical engineering, electrical engineering, automation technology or computer science and have the following knowledge and skills: basic knowledge in the field of control and automation technology or data science, ability to work independently and scientifically, ability to work in a team, flexibility, good written and spoken English. Ihre Aufgaben In today's manufacturing industry, process automation is becoming increasingly important, as it not only makes a significant contribution to reducing manual interventions—and thereby saving manpower—but is also essential for implementing sustainability strategies. Alongside improving efficiency, resource conservation and the circular economy are taking center stage in modern production processes. Through the targeted use of modern automation technologies, complemented by machine learning, processes are not only designed to be more efficient and resource-saving, but are also systematically monitored and optimized with regard to their sustainability. This results in a significant increase in productivity and enables the consistent achievement of high quality standards without the need for extensive manual controls. Our research aims to develop innovative control strategies that successfully balance maximum product quality with the steadfast implementation of the circular economy, sustainability, and resource conservation in order to meet the increasing demands of the market. Especially for thin, metallic strips that are used in various technological fields, such as communications or high-frequency technology, controlling surface roughness is of crucial importance. These surface properties—such as paint adhesion, surface gloss, and tribological indicators like the coefficient of friction—decisively influence both the functionality and the external appearance of the final product. To address the complex requirements of these applications, our research combines state-of-the-art process automation concepts with innovative control strategies and machine learning approaches. In collaboration with the Institute of Metal Forming at RWTH Aachen University, we are developing methods that utilize precise measurement techniques, adaptive control algorithms, and data-driven machine learning models to enable continuous monitoring and regulation of surface parameters. For example, real-time measurements can capture the roughness of the metallic strips and feed this data directly into the control system to achieve desired surface qualities—such as optimal paint adhesion and a uniform gloss. Simultaneously, machine learning algorithms facilitate the early detection and prediction of wear phenomena, for instance on working rollers, thereby optimizing maintenance intervals and reducing unplanned downtimes. As part of the doctoral position, you will research novel approaches to application-oriented automation and control together with interdisciplinary project partners of the institute of Metal Forming and industry. Your work will cover a wide range of issues in the following areas: modeling technical systems, machine learning, self-optimizing and learning controls, implementation of algorithms in prototype software, experimental validation on real test beds. You will present your research findings at international conferences as well as in renowned scientific journals. Furthermore, you will share your knowledge through teaching activities for students. 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