Karlsruhe Institute of Technology 
Eggenstein-Leopoldshafen, Germany Proje Açıklaması
The Institute for Microstructure Technology (IMT) conducts research on innovative micro- and nanoscale technologies for applications in the fields of sensor technology, electronics, materials science, and biomedical systems. A particular focus of research is on the development of novel technologies for magnetic resonance, especially for miniaturized NMR, MRI, and EPR systems, as well as highly integrated hardware solutions for spectroscopy and imaging.
Your tasks
As part of the Collaborative Research Centre Hyperion, you will help develop a system that makes it possible to expose liquids in microfluidic channels to electric fields inside a magnetic resonance tomograph. Only non-magnetic materials may be used for this purpose, and the effects of the different materials on field homogeneity must be taken into account. The finished system is intended to enable MR measurements that are coordinated with the electrical actuation of the electrodes.
Your area of responsibility includes in particular:
- Development and optimization of reconfigurable, miniaturized electrodes in a microfluidic system
- Development, simulation, and optimization of microfluidic systems
- Design of digitally programmable drive electronics for the electrodes in the microfluidic chip.
- Integration of the system into magnetic resonance tomographs by means of fluidic and electrical actuation.
- Publishing results in international journals, attending scientific conferences, and participating in networking meetings of the Hyperion project.
The position offers the opportunity to pursue a part-time doctorate alongside your work.
Your profile
- M.Sc. in Electrical Engineering, Electronics, Microelectronics, Physics, Microsystems Engineering, or Mechatronics.
- Knowledge of analog and digital electronics.
- Knowledge of Finite Element Methods (FEM) and CFD simulations, as well as experience with NMR, microfabrication technologies, signal processing, and data-driven methods (e.g., deep learning) are an advantage.
- Independent scientific work, teamwork skills, and interest in interdisciplinary questions and new topics.
- Good written and spoken English skills.
