Department of Electrical Engineering: Three assistant/associate professor positions in Machine LearningWelcome to the Brainport technology region.
Eindhoven and the Brainport technology region are a hotbed of cross-pollination and creativity where pure science is given real world meaning. The university's culture of curiosity breaks down barriers between disciplines so that breakthroughs are celebrated as one and challenges are confronted from all angles. Your location at the heart of a thriving community of engineers from some of the world's most innovative companies keeps you grounded and ensures streamlined, relevant solutions.
The Department of Electrical Engineering at the Eindhoven University of Technology (TU/e) invites applications for tenure-track assistant/associate professor positions in Efficient Machine learning. The start date in 2018 is flexible. The screening of applications will start on June 30th, 2018.
TU/e is a globally highly-ranked technical university, in particular with regards to collaboration with industry and the global impact of its scientific research (see
https://goo.gl/3AucT3).
TU/e has identified Artificial Intelligence (AI) as one of the key multi-disciplinary research objectives in its R&D roadmap to 2030. In that realm, various research groups across the university have developed active and visible AI-related research initiatives over the past few years. There is an ongoing initiative both at the university board level and the faculty level to strengthen our research and education in this direction.
We look for candidates with solid expertise in machine learning technologies who are interested in contributing to scientific innovation and education in any of the following research directions:
Efficient machine learning (ML) / deep learning (DL) systems. (This candidate will get hosted at the ES group, link): This position relates in particular to the development of energy and resource efficient ML networks, algorithms and circuits, and advanced mapping techniques for the efficient execution of ML algorithms and networks on different processing targets.Machine learning for dynamical systems (Hosted at the CS group, link): addressing dynamic behavior of engineering systems and related stochastic properties in applied modelling, control and estimation problems with a strong link to applications in smart industries and high-tech systems, robotics, autonomous vehicles (cars, drones).Intelligent information processing in streaming data (Hosted at the SPS group, link): e.g. audio, video, communication data or physiological measurements. We have an active interest in probabilistic (Bayesian) and neuroscience-inspired machine learning technology as well as deep learning technology for a wide range of signal processing applications.As a newly appointed faculty member, you will get the opportunity to start your own lab on ML and maintain an active collaboration on the ML-related research activities at the department and beyond. Next to the quality and impact of conducted research, your educational skills are very important since we expect you to develop and teach ML courses at all levels.