The Eindhoven Artificial Intelligence Systems Institute (EAISI) is funding 2 PhD positions for the highly innovative BayesBrain project, in collaboration with the departments of
- Mechanical Engineering,
- Biomedical Engineering,
- Electrical Engineering, and
- Mathematics and Computer Science
To investigate the crosstalk between cells and computers, we will develop an interface, which facilitates the connection between a neural cell network and a machine learning algorithm. The announced PhD position here focuses on devising the hardware of a compartmentalized microfluidic chip on microelectrode arrays (MEAs). Specially designed MEAs will form the interface to the in-silico control agents for the purpose of building a hybrid AI computer. For this position, relevant background knowledge and hands-on skills on neural cell culture, integrated microfluidics, sensors & actuators (MEA technology), and fast and high-quality data acquisition systems is essential. The successful candidate will critically review the state of the art (high-density versus low-density MEA tech) and creatively design a simple-to-use microfluidic platform that will have a central role in building the hard-ware of the hybrid AI system of connected neural circuits. We wished to address circuits and ideally individually cells in the neurocircuits by voltage or current injection. Furthermore, the techniques developed will have a wider applicability in material and toxicity screening, drug development and personalized medicine and the discovery of novel neuro-therapeutic interventions. The successful candidate will be hosted at the Department of Mechanical Engineering and co-supervised by the Department of Biomedical Engineering.
The successful candidate is proficient and enthusiastic about the science and technology of microfabrication and working with cell cultures. It is expected that candidates have completed a Bachelor and Master study with high relevance to both this topics with a strong focus in experimental skills.
Thanks to the theoretical success of AI concepts in the last century, computational hardware allows us now to perform complex tasks. However, these capabilities come at a very high computational cost due to a vast amount of energy consumption in the purely electrically designed circuitries. On the other hand, our brain performs complex tasks in a highly efficient manner. Taking the free energy minimization of brain as our inspiration, we aim for performing complex tasks by the combined use of neurocircuit biology and AI. Thus, the Ph.D. candidate will contribute to the hardware development of a microfluidic brain-on-chip interface. For this novel microfluidic brain-on-chip, a critical reflection on engineered biomaterials and electrically embedded microfluidic platforms and their fabrication methods, integrated multi electrode array approaches, as well as fast and high-quality data acquisition systems are required. In this project, the candidate will design innovative ways to create the microfluidic platform, build the system and culture neuronal cell networks themselves.
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