Postdoctoral Position on Neuromorphic Bioelectronics for Brain-Computer Interfaces, Neuro-prosthetics, and Neurological Monitoring

SDU - University of Southern Denmark

Odense, Denmark

We are seeking a highly motivated candidate to develop and implement Spiking Neural Networks (SNNs) and State Space Models (SSMs) tailored for the real-time processing of complex brain signals such as EEG or neural spikes. This role focuses on bridging brain-inspired computation with energy-efficient hardware implementations for edge and wearable healthcare applications.

The research is applicable across a broad range of neural-interface technologies, including Brain–Computer Interfaces (BCIs), neural prosthetics, neurological disorder monitoring (e.g. epilepsy), and intelligent neurotechnology systems. The overarching goal of the research project is to enable seamless human–machine interaction by interpreting brain activity (e.g., EEG signals) in real time using energy-efficient neuromorphic hardware.

The successful candidate will work on the design and hardware realization of neuromorphic systems, bridging neuroscience and electronics. The project combines expertise in circuit design, machine learning, and neuromorphic computing.

As a Digital IC Design Postdoc, your key responsibilities will be:

• Extract the requirements and specifications in close collaboration with project partners.
• Develop algorithms based on SNNs, SSMs, and neuromorphic AI for neural signal processing.
• Participate in the full design cycle, from architecture to implementation, of a CMOS-based digital neuromorphic processor.
• Contribute to the design of a test setup for prototype validation in collaboration with the PhD student who is designing the analog front end.
• Clearly document design specifications, trade-offs, and measurement results.

What we expect:

Applicants should hold a PhD in Electronic Engineering (the degree should have been completed within the last 6 years at most):

• Expertise in neuromorphic computing and neural signal processing, with hands-on experience utilizing brain-inspired Spiking Neural Networks (SNNs) and hardware-efficient State Space Models (SSMs) for complex bio-signals.
• Strong knowledge of digital IC and VLSI system design, including hardware description languages (Verilog/VHDL), circuit simulation (SPICE), and industry-standard EDA tools.
• Experience with hardware–software co-design, FPGA implementation, neuromorphic hardware, or edge AI deployment is an advantage.
• A strong research track record demonstrated through peer-reviewed publications and the ability to work effectively in collaborative research environments.
• Team player with strong collaboration skills.
• All candidates must demonstrate excellent verbal and written English skills, along with very good communication abilities.