Specific informationTeach the education components of the education program;Teach periodic maintenance of the assigned education components;Indicate improvement possibilities for the assigned education components;Carry out independent research for the purpose of science, companies - where possible - government and the business sector.Contribute to the recruitment of second and third flow of funds;Provide specific guidance to the academic personnel in carrying out research;Provide leadership to work groups, committees or project teams in the groups.
Autonomous engineering systems are being developed at high pace worldwide with examples being automated vehicles, drones and robotics for manufacturing, care, cure, agricultural and domestic applications. Both industry and society foster high expectations on the future impact of autonomous systems. Industrial examples are fully automated distribution of goods in harbors, warehouses and factory floors, and the automated inspection of crops in agriculture. Societal impact is envisioned, e.g., in automated highways or in hospitals where surgery robots cooperate with surgeons or patient support systems reaching higher levels of autonomy aiming at cost reduction and improved care or city-wide parcel delivery by drones. In general, system autonomy allows, on the one hand, to take the (expert) human out of the loop to achieve increased performance, safety and efficiency of engineering systems, while, on the other hand, it allows such systems to interact in more complex, real-world environments where interaction with (non-expert) humans is actually required. Both aspects will allow for a step change in the pervasiveness of engineering systems in industry and society.
Key technologies for autonomous systems are currently investigated and developed at TU/e within the division Dynamical Systems Design (DSD) of the Mechanical Engineering Department in close collaboration with the TU/e High-Tech Systems Center. Long-standing application-related research lines are being maintained on topics that, on the one hand, would benefit from fundamentally novel developments in the scope of dynamics, decision and control and, on the other hand, would serve as excellent ways to valorize such research in relevant application domains, e.g., autonomous and cooperative driving, precision-agriculture (high tech to feed the world), robotics and mechatronics, digital twin for smart industry & advanced manufacturing, high-tech systems, health applications, smart energy systems, automation of distribution centers, acoustics, smart materials, etc.
This research theme provides ample opportunity for fundamental, interdisciplinary research and valorization in a wide range of applications, many of which are strongly rooted in the Dutch high-tech & manufacturing industry, and the agriculture and transportation sectors.
The Department of Mechanical Engineering of the TU/e seeks to hire multiple outstanding faculty members at all levels within the field of Dynamics, Decision and Control, who have the ambition to have a leading role in addressing the above-mentioned challenges and trends, as well as a strong research interest in the following topics.
Modeling and Control of Large-scale Networked Systems (Cyber-Physical Systems of Systems):
An important challenge in future autonomous control systems is how to deal with the complexity and heterogeneity of large-scale applications such as intelligent transportation systems, robotic swarms in agriculture, wide-area manufacturing and industrial plants. These systems of systems or multi-agent systems consist of networks of dynamical subsystems described by multi-scale models. A fundamentally new systems and control theory is needed to analyze these systems and to design distributed and multi-level control systems communicating over (wireless) communication channels connecting such systems. Research in this field requires an inherently multi-disciplinary approach relying on expertise from control theory, graph theory, hybrid systems, computer science, operations research, and optimization.
Data-based Dynamical Modelling and Control:
A common challenge in autonomous systems is that such systems do no longer operate in well-defined purely technological environments, but rather in interaction with other complex, both technological and societal, systems and humans. This gives rise to need for breakthroughs in system responsiveness to uncertain environments and changing circumstances. Such breakthroughs are expected from the fields of data-based modelling and control technology and online optimization, exploiting the increasing availability of big data to make such systems intelligent.
Control for Autonomous and Cooperative Vehicles:
Efficient and safe autonomous transportation systems of the future will require an increased level of autonomy of individual vehicles, e.g., on highways and in urbans areas, but also in distribution centers, and harbors. The realization of such technology urges the need for innovation on the intelligent control of individual vehicles, varying from driver support systems to fully autonomous driving and cooperative driving solutions. In support of such innovations significant synergy with state-of-the-art communication and automotive sensing developments and in-depth knowledge of vehicle dynamics is needed.
One of the main challenges in autonomous robotics is to design robotic systems that can deal with an open world that allows for variations and changes over time. The essential knowledge hub in such robotic systems is the robotic world model that allows the robot to reason about performing its variable task in its varying environment. This position focuses on world modeling for autonomous robotic systems including knowledge representation, reasoning, and action planning in motion and perception, aiming for robust performance in an open world.
Distributed sensing, sensor fusion in combination with AI:
Continuous developments towards plug-in, cost-efficient sensor technologies make the use of networks of distributed sensor networks feasible for numerous applications ranging from autonomous monitoring of traffic events to smart diagnostics of industrial machinery and assisting the communication with and tracking of autonomous agents. The challenge is to minimize the amount of sensors required in dynamical systems by developing data fusion algorithms, pattern recognition methods and decision schemes tailored to each specific application.
Candidates can apply to (tenure-track) assistant, associate and full professor positions and are expected to be experienced in at least one of the areas mentioned above. She/he should have the ambition to contribute to the creation of a strong, internationally renowned research group, while fostering a collaborative network with other academics working on the topic within the department, the university and industry. The candidate should contribute to the progress in the field of dynamics, decision and control, in order to be able to make the step to further penetration of system autonomy in industry and society.
The candidate will contribute to the existing BSc program Mechanical Engineering and the MSc programs Mechanical Engineering (ME) and Systems and Control (S&C) within the department.
The candidate will strongly co-operate with existing groups in the Department. The department fosters a personnel strategy stimulating personal growth towards faculty forming independent groups within larger coherent sections.
The candidate will be connected to the High Tech Systems Center within the University. He/she will also be embedded in research schools of the Dutch Institute of Systems and Control (DISC) and/or Engineering Mechanics (EM), depending on the position.
These function elements hold for researchers at an assistant professor level. For candidates at Associate Professor level or Full Professor level, additional elements and elements at a higher level are expected.