PhD position on microfabricated fluidic sensors enhanced by machine learning
In this project, you will work on cutting-edge multiparameter flow sensing systems. Applying deep symbolic AI to microfluidic sensor data will fundamentally improve the design and use of these systems in real-life applications.
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 next step in this research is to design and fabricate novel integrated sensors that are more compatible with machine learning. After fabrication, a comprehensive experimental setup is required to produce a dataset for the machine learning algorithms.
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 micro-reactors, or in the food industry.
In this PhD research, you will combine the latest scientific developments on fluidic sensors, microtechnology and chip design with your own creativity. Novel types of flow sensors, in-line pressure sensors and other fluidic sensors have to be integrated into the same chip. You will get access to the state-of-the-art MESA+ Nanolab cleanroom facilities to fabricate these devices using micro technologies like deep reactive ion etching and low-pressure chemical vapor deposition. Furthermore, you will be in charge of your own experiments and comprehensively characterise your samples to generate the dataset. This also includes data analysis and application of preprocessing steps (e.g., feature extraction) as preparation for the machine learning algorithms.
In this project, you will closely work together with a second PhD candidate (in the Pervasive Systems research group) who will focus on the machine learning side of this research project. Furthermore, you will work together with experts on sensor chip design and microfabrication.
Information and application
Are you interested in this position? Please send your application via the 'Apply now' button below before 21 October 2023, and include:
- A cover letter (maximum 1 page A4), emphasizing your specific interest, qualifications, and motivation to apply for this position.
- A Curriculum Vitae, including a list of all courses attended and grades obtained, and, if applicable, a list of publications and references.
For more information regarding this position, you are welcome to contact (Dennis Alveringh (d.alveringh@utwente.nl)
Interviews are scheduled on the 20th of November.
About the department
The group of Integrated Devices and Systems (IDS) studies the heart of microdevices: electronic and electromechanical components. Our investigations range from materials science and microfabrication to device design and characterization. IDS is part of the Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS). We work on very interdisciplinary topics in our group, with electrical engineers, physicists, chemists and other experts. In our research, we work together with many different departments within the MESA+ institute, the university and industry. Our colleages are typically both passionate and easy going.
The candidate is expected to collaborate with project partners including the Pervasive Systems (PS) group, Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), at the University of Twente in the Netherlands.
About the organisation
The faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) uses mathematics, electronics and computer technology to contribute to the development of Information and Communication Technology (ICT). With ICT present in almost every device and product we use nowadays, we embrace our role as contributors to a broad range of societal activities and as pioneers of tomorrow's digital society. As part of a people-first tech university that aims to shape society, individuals and connections, our faculty works together intensively with industrial partners and researchers in the Netherlands and abroad, and conducts extensive research for external commissioning parties and funders. Our research has a high profile both in the Netherlands and internationally. It has been accommodated in three multidisciplinary UT research institutes: Mesa+ Institute, TechMed Centre and Digital Society Institute.
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