A new ELLIIT-financed tenure-track position is available at the Department of Electrical Engineering, Vehicular Systems at Linköping University. Core of the subject area is classification of observed behavior of a technical system and detection of faulty, undesired, or dangerous states. For this a combination of model-based and data-driven techniques are used, and typical tools are the laws of physics and their structure that is integrated with machine learning techniques and methods from mathematical statistics and logic. With an increasing degree of autonomy and connected industrial systems, for example in transport and energy, there is a need to optimize efficiency, availability, and reliability of these systems. An autonomous system must, automatically and reliably, identify and act reliably in unseen and unusual situations with disturbances and faults that results in reduced performance. An important and directly related subject is fault tolerant control and predictive maintenance. Machine learning is a class of methods that has proven useful in modelling information in recorded data. A characteristic of technical dynamic systems is that it is often difficult, sometimes impossible, to collect training data that represents the full set of scenarios the model is expected to monitor. These scenarios include rare situations, that may not ever happen in training, such as faulty states or critical accidents. At the same time, there is often available knowledge about the system in the form of mathematical models and basic physical constraints and fusing this knowledge with available data is key to achieve performant systems.
The position requires the ability to do independent research in diagnostics and prognostics at an international level, do build an externally funded research program, and to collaborate with industry and the community in research and data. The position also requires supervision of PhD students and master thesis workers, in addition to teaching and developing courses at the division. Some administrative work is also included. The teaching and work assignments require the ability to communicate both in Swedish and English.
