/Mind mapping with acoustic tags

Mind mapping with acoustic tags

PhD - Leuven | More than two weeks ago

Develop a non-invasive technology to unravel brain activity

The brain is an organ so complex that it is difficult to even fathom the intricacies of its function. While the past decade has seen tremendous developments in neuroscience, we are still merely dabbling in this domain. This is vastly due to our lack of proper tools to gain information about the brain. It is that much striking given that interfacing with the brain

Imec is at the forefront of neuroscience research by its development of neuroprobes with world-leading electrode count, density and signal quality. However, the enabled sensing is limited to the proximity to the inserted probes and require obviously invasive practices. Novel modalities are therefore under development that will enhance the spatio-temporal resolution and reach of these brain interfaces.

The purpose of this PhD is to develop and demonstrate ultrasound hologram projection for complex brain tagging to enhance the spatial resolution and reach of functional imaging technique like functional near infrared spectroscopy (fNIRS). Using proprietary mature state-of-the-art piezoelectric Micromachined Ultrasound Transducers (pMUTs) technologies, the candidate will generate low intensity focused ultrasound (LIFU) tags in head model (brain-skull phantoms). These introduce minute periodic variations of the local electromagnetic properties of the brain that can be picked up in the imaging signals obtained non-invasively. This enhances the imaging resolution at depth to the resolution of the acoustic hologram projected.


The ideal candidate should have:

  • A background in nanoengineering, bioengineering, Physics
  • a good understanding of linear acoustics, numerical methods and programming
  • solid knowledge of ultrasound transducer devices and technology
  • experience with acoustic experimental setups


Required background: Engineering Science


Type of work: 40%modeling/simulation 50% experimental, 10% literature

Supervisor: Liesbet Lagae

Co-supervisor: Xavier Rottenberg

Daily advisor: Veronique Rochus

The reference code for this position is 2022-096. Mention this reference code on your application form.