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.

• You are highly motivated and an enthusiastic researcher with independent thinking, critical analytical skills and a decent amount of creativity;
• You have a master’s degree in Electrical Engineering, Physics, Mechanical Engineering or a related study;
• Basic knowledge of microtechnology (e.g., etching, lithography) is preferred.
• Basic experience in (MEMS) chip design is preferred using mask design software like CleWin, KLayout, L-Edit, Cadence or Layout Editor;
• Affinity in the use and automation of electronic lab equipment is preferred.
• Basic programming and data analysis skills are required: e.g., MATLAB, Python or C;
• You have good team spirit and like to work in an internationally oriented and very interdisciplinary environment;
• You are interested in science and are driven to contribute to the scientific community in your field.

• As a PhD student at UT, you will be appointed to a full-time position for four years, with a qualifier in the first year, within a very stimulating and exciting scientific environment;
• The University offers a dynamic ecosystem with enthusiastic colleagues;
• Your salary and associated conditions are in accordance with the collective labour agreement for Dutch universities (CAO-NU);
• You will receive a gross monthly salary ranging from € 2.770,- (first year) to € 3.539,- (fourth year);
• There are excellent benefits including a holiday allowance of 8% of the gross annual salary, an end-of-year bonus of 8.3%, and a solid pension scheme;
• A minimum of 232 leave hours in case of full-time employment based on a formal workweek of 38 hours. A full-time employment in practice means 40 hours a week, therefore resulting in 96 extra leave hours on an annual basis.
• Free access to sports facilities on campus
• A family-friendly institution that offers parental leave (both paid and unpaid);
• You will have a training programme as part of the Twente Graduate School where you and your supervisors will determine a plan for a suitable education and supervision;
• We encourage a high degree of responsibility and independence while collaborating with close colleagues, researchers and other staff.

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.

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.