2 dagen geleden - Erasmus Universiteit Rotterdam - Rotterdam
Job description We are expanding our team of Business Information Management faculty members to complement our ongoing research into this increasingly …
Uncertainty quantification, disturbance modelling,
A PhD degree in systems and control, applied mathematics, mechanical engineering, electrical engineering, or a related field
Uncertainty quantification and disturbance modelling are essential parts of the data-driven control cycle of multi-disciplinary systems. They addresses the question of what is possible and what is important, respectively. Therefore, the combination of uncertainty descriptions with multi-disciplinary system models is essential to enable reliable, robust, and efficient decision making. This fundamental framework makes it possible to develop robust, data-driven control systems for demanding industrial application fields, including large-scale mechatronic systems, dynamic positioning systems, and ocean/-wind energy harvesting systems.
Currently, we seek an expert in the field of data-driven modelling and/or control with a solid background in the field of (robust) control engineering and/or nonlinear system identification.
Cooperation with other members of the scientific staff and establishing relationships with practitioners are important aspects of this position. You will also contribute to teaching at the MSc and BSc levels. Every new Assistant/Associate Professor is given a tenure-track position.
A tenure track, a process leading up to a permanent appointment with the prospect of becoming an Associate or full Professor, offers young, talented academics a clear and attractive career path. During the tenure track, you will have the opportunity to develop into an internationally acknowledged and recognised academic. We offer a structured career and personal development programme designed to offer individual academics as much support as possible. For more information about the tenure track and the personal development programme, please visit www.tudelft.nl/tenuretrack.
The successful candidate must have the following qualifications:
• A PhD degree in systems and control, applied mathematics, mechanical engineering, electrical engineering, or a related field
• Fundamental knowledge of control systems theory and/or system identification
• The capacity to communicate effectively with peers, students and stakeholders in the application field
• An excellent track record in scientific publications, acquisition and management of externally funded research projects, a clear view on required future developments, aiming at a high degree of sustainability in the application field, and experience as a post-doctoral researcher
• Fluency in English and the willingness to learn Dutch within three years
• An open personality and good communication skills in English.
Delft University of Technology (the TU Delft) is a multifaceted institution offering education and carrying out research in the technical sciences at an internationally recognised level. Education, research and design are strongly oriented towards applicability. The TU Delft develops technologies for future generations, focusing on sustainability, safety and economic vitality. At the TU Delft you will work in an environment where technical sciences and society converge. The TU Delft comprises eight faculties, unique laboratories, research institutes and schools.
Mechanical, Maritime and Materials Engineering
The Delft Centre for Systems and Control (DCSC) coordinates the education and research activities in the fields of systems and control at the Delft University of Technology. The Centre's mission is to conduct fundamental research in systems dynamics and control, involving dynamic modelling, advanced control theory, optimisation and signal analysis. This undertaking is motivated by advanced technology developments in several (emerging) application fields.
Within the data-driven control section of DCSC the focus is on the analysis and decision making for large-scale (in size), multi-disciplinary, dynamical systems. It addresses the question of what model complexity is necessary for all individual system components in order to use data-driven models for reliable and robust model-based diagnostics, parameter estimation, monitoring and control.