For many medical and industrial applications, there is a need for versatile microfluidic sensing systems that are able to extract more information from fluid mixtures than currently possible. By using microfabrication technologies, multiple sensors – e.g. for flow rate, pressure and density – can be integrated into a single chip. To calculate e.g. the viscosity from the flow rate and pressure, conventional data processing methods involve filtering the raw sensor signals, and calibration of the individual sensors and physical models. These methods are time-consuming, not always applicable, and leave potentially relevant information undiscovered. Therefore, it is now a necessity to explore recent work in symbolic Artificial Intelligence (AI) to overcome these limitations and allow for real-time fluid data processing by using a combination of deep neural networks and physics in flow sensing.
The goal of this project is the realization of a demonstrator system containing multiple sensing structures together with a trained neural network, which outperforms the state-of-the-art multiparameter systems for real-time quality control of products made in chemical or pharmaceutical microreactors, or in the food industry.
The Pervasive Systems research group at the University of Twente is looking for a PhD candidate to perform research and development on a multidisciplinary project involving deep symbolic AI and microfabricated fluidic sensors.
The main research activities are:
- Conduct research in deep symbolic AI for microfabricated fluidic sensors, including but not limited to designing and implementing lightweight but accurate algorithms and models, conducting experiments, analyzing data, and interpreting results.
- Collaborate with the team to develop and optimize microfabrication processes for the sensors.
- Develop and test new sensor designs and configurations, and evaluate their performance.
- Write technical reports and research papers for publication in top-tier journals and conferences (Percom, Ubicomp, IJCAI, AAAI, NIPS, ICML).
The candidate is expected to collaborate with project partners including the Integrated Devices and Systems (IDS) group, Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), at the University of Twente in the Netherlands.