PhD - Leuven | More than two weeks ago
Modern biomedical instrumentation requires the unchallenged levels of precision and failproof reliability. In that way, a solution to prevent biomedical sensor failure may be found in developing a next-generation robust and reliable measurement system for a practical diagnosis. For that, a use of the redundant ultrasound sensor array is proposed, which is based on a use of multiple MEMS ultrasound devices. Combination of identical devices in a complex interactive system has previously shown to bring the required benefits: first, if one of the devices fails other ones can assure the continuity of a correct functioning of the sensor system; second, a significantly higher precision of measurement can be achieved by applying an advanced signal processing techniques known as sensor fusion routines.
The goal of this PhD thesis is to build a redundant ultrasound array and develop a sensor fusion technique for a reliable and precise next-generation biomedical diagnosis. This thesis will be performed in collaboration with the company Bloomlife.
Scope of the PhD
The PhD topic aims at developing a performant and reliable system for measuring mechanical characteristics of a human tissues and body liquids with ultrasound. In the context of this project, the candidate will develop:
You are a highly motivated student, with background in nano-engineering, physics, material science, electrical engineering, or related. You have an interest in the designing ultrasound transducer, in data analysis and in characterization of acoustical performance of developed devices. It is expected that you will present results regularly. You are a team player and have good communication skills as you will work in a multidisciplinary and multicultural team spanning several imec departments. Given the international character of imec, an excellent knowledge of English is a must.
Required background: nano-engineering, physics, materials science or electrical engineering with strong affinity for device physics
Type of work: 15% literature study, 30% modeling, 20% design, 35% characterization
Supervisor: Chris Van Hoof
Co-supervisor: Xavier Rottenberg
Daily advisor: Veronique Rochus, Bogdan Vysotskyi
The reference code for this position is 2022-101. Mention this reference code on your application form.