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Doctoral Grant (BOF), Real-time Embedded Systems - 2020BAPDOCPROEX008Posted by: University of Antwerp
Posted date: 2020-Jan-16
The Faculty of Applied Engineering is seeking to fill a full-time (100%) vacancy in the Department of Electronics-ICT for a
Doctoral Grant by the University Research Fund (BOF) in the area of Real-time Embedded Systems
Cosys-lab performs research on the embedded software, hardware and communication technology of cyber-physical systems (CPS). CPS are systems that combine computational and networking components together with physical components. As such, designing a CPS requires a multi-disciplinary approach. CoSys-Lab also focuses on the development processes of these CPS components. The main application domains of our research are machine building, automotive and health & care. You will be part of the team that works on model-based systems engineering for CPS.
We contribute to model-based techniques for the realisation of embedded technology for CPS. To achieve an optimal realisation of these embedded systems, it is essential that also the physical (mechanical, electromagnetic, hydraulic, …) components of the CPS and the CPS's environment are modelled jointly with the embedded components.
The project aims to create the foundations for applying adaptive abstraction and approximation at run-time for cyber-physical systems (of systems): To allow decisions to be made in a CPS (strategic control, tactical control and, low-level control), decision models are used. These models use input from sensors, but also from other supporting processes, e.g. predictions over the state of its context, to come to a control decision. The decision processes are implemented in software that runs on embedded hardware and is commonly real-time constrained, meaning that the time at which the decision is taken, is as import as the decision itself. In literature several techniques are available to reduce the computational cost of executing models by using abstraction and approximation (e.g. surrogate modelling). This reduced cost would allow the process to come to a decision more easily (scheduling) and would require less computational resources. However, we still need to be sure that the decision process is robust against approximations and uncertainties in these models. Furthermore, an approximated and/or abstracted model is most probable not valid in all the different contexts the system will be in. To enable this, the system should be able to switch at run-time between different abstractions and approximations. Therefore, this project will create the foundations to reason about dynamically adapting the decision models and prediction models with different abstractions and approximations depending on the context of the system. The project will result in a framework with supporting modelling languages, methods and proof-of-concept tools to reason on the trade-off between uncertainty (from the approximation) and the real-time behaviour of the system.
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