Assistant/Associate Professor Multi-dimensional Signal Processing and Control
Updated: 05 Jul 2019
Global digitalization has greatly expanded the availability of high-performance multi-dimensional sensor systems. To increase the performance and robustness of such complex multi-disciplinary control systems in high-tech manufacturing, safety systems, aerospace, and the biomedical sciences, it is essential to exploit these vast amounts of data efficiently. The high-dimensional nature of these signals and systems poses great challenges in terms of signal processing, system identification, and control. A comprehensive approach, combining insights from several scientific areas, cross-fertilized with signal processing and control engineering, is essential.
This position is located in the Numerics for Control and Identification (N4CI) section within DCSC. The position should expand and/or strengthen N4C’s current activities in the area of signal analysis, identification, and control on one side and application areas including adaptive optics, microscopy, molecular imaging, mass spectrometry, fibre optics, and water waves on the other side.
Applicants should possess the following qualification and attributes:
- A Ph.D. degree in Systems and Control, Electrical Engineering, Physics, Applied Mathematics, Computer Science or another relevant engineering discipline, with an outstanding scholar record and a strong commitment to excellence in research and teaching.
- Experience in a broad range of signal processing applications such as fiber optics and adaptive optics.
With a strong background in the broad area of Signal Processing & Information Engineering, preferably having expertise in one or more of the following (but not limited to):
- Advanced and emerging areas of signal processing, e.g., adaptive sensing, compressive sensing.
- Statistical learning and inference, e.g., information-theoretic learning, dimensionality reduction, fundamental limits.
- Machine learning and artificial intelligence, e.g., deep learning techniques and theory, Bayesian learning, active learning, reinforcement learning, distributed learning.
- Applications of signal processing in high-tech manufacturing, security, safety systems, automotive, aerospace, or biomedical systems.
- Signal processing for control integrating signal processing methods with advanced controller engineering methodologies, like adaptive or reinforcement learning control, scenario based methods for distributed optimization.
The tenure-track position is offered for six years. The section leader, department leaders and the successful candidate will agree upon expected performance and (soft) skills. Formal feedback on performance and skills will be provided during annual assessment meetings and the mid-term evaluation. Based on the performance indicators agreed upon at the start of the appointment, a decision will be made by the fifth year whether to offer a permanent faculty position. If the performance and skills are evaluated positively at the end of the tenure track, a permanent Assistant Professor position will be offered.
TU Delft offers a customizable compensation package, a discount for health insurance and sport memberships, and a monthly work costs contribution. Flexible work schedules can be arranged. An International Children’s Centre offers childcare and an international primary school. Dual Career Services offers support to accompanying partners.
Salary - ranging from 3.637 to 5.656 for Assistant Professor and from 5.039 to 6.738 for Associate Professor gross a month (full time based) - and benefits are in accordance with the Collective Labour Agreement for Dutch Universities.
TU Delft sets specific standards for the English competency of the teaching staff. TU Delft offers training to improve English competency. Inspiring, excellent education is one of our central aims. If you have less than five years of experience and do not yet have your teaching certificate, we allow you up to three years to obtain this.
TU Delft creates equal opportunities and encourages women to apply.
32 - 38 hours per week