Neuromorphic computing for monitoring and control in sustainable and smart production systems
OPEN POSITION
APPLICATION ID: ALL10
What we are looking for:
We are seeking innovative research proposals that explore advanced methodologies for adversarial generative AI models in the context of CPHS, and novel approaches for reliability analysis and life estimation in cyber-physical-human systems using digital twins.
The context:
The advancement of artificial intelligence (AI) technologies has profoundly impacted the development of cyber-physical systems/IoT, especially in the realm of electronic components and the related manufacturing systems. The integration of AI with these systems presents unparalleled opportunities for innovation, particularly by using digital twins and the prognostic of remaining useful life. There are also new challenges in terms of modeling, reliability and decision-making, necessitating advanced research in the field.
The use of AI can impact the electronics production from the materials, to the device level up to the circuits and systems level.
The problem to address:
The primary challenge lies in the design and development of AI-based models as core elements of digital twins, with a focus on simulation and prediction from materials, electronic component behaviour, circuit design up, to cyber-physical manufacturing systems in the realm of electronics. This involves not only creating accurate and efficient models but also ensuring their reliability and robustness in predicting and analyzing the remaining useful life of these complex systems.
Objectives:
- To investigate the use of AI as tools for enhancing the design, performance and reliability of electronic components, circuits and systems.
- To Investigate the Potential of deep learning and generative AI in enhancing the performance of cyber-physical systems in the realm of electronics.
- To develop models or prototypes that demonstrate the effectiveness of AI-driven models in real-world electronic applications.
Expected Outcomes:
Detailed Analysis and New Methods that address:
- The interaction between AI models and cyber-physical-systems.
- The impact of digital twins on the prediction and management of system lifecycles.
- Innovative Solutions or Prototypes Employing Deep learning hybrid methods and/or generative AI models in practical AI applications within CPS/IoT.
- Digital twins for enhanced decision-making and predictive analysis in electronics.
- Use of electronics accelerators and neuromorphic hardware to enhance digital twins’ performance.
