Neuromorphic photonics is emerging as a promising paradigm to overcome bottlenecks in conventional computing, offering ultra-fast and low-energy information processing. Recent advances include both spiking and deep learning schemes implemented on photonic chips. However, integration challenges and material limitations remain. Non-volatile photonic elements and hybrid active–passive platforms are gaining traction, and momentum is building toward scalable architectures and electronic–photonic co-design.
InformationDespite significant progress, several key challenges remain open in the development of next-generation intelligent photonic systems. Addressing these challenges defines a set of promising research directions that goes from the development of mature hybrid-integrated active–passive photonic platforms to the systematic exploitation of physical parallelism, from the realization of stronger all-optical nonlinearities and scalable synaptic devices to the design of efficient photonic memory architectures to support neuromorphic training functions. Further, High-efficiency neuromorphic photonics will require the convergence of neuromorphic computing and deep learning on photonic hardware, the development of optically informed algorithms that explicitly account for noise and limited precision inherent to photonic systems, and stronger coupling to real-world application layers through system-level demonstrators.
Therefore, within this positio,n you will take a holistic approach that considers not only individual components (e.g., emerging materials and devices) but also their interaction at the system level — encompassing architecture, electronic integration, algorithms, and end-use applications.
As Assistant Professor in Photonic Neural Networks, you will:
- Develop an independent research program in hybrid neuromorphic photonics, spanning the full protocol stack — from physical-layer innovation and hybrid integration (optics, photonics, electronics, novel materials) to higher-layer network optimization, inference, and training in real time.
- Investigate novel architectures such as hybrid network topologies, neural network mapping in the optical domain, spiking (and non) encoding and strategies to address photonic engine noise, limited resolution, and system-level robustness.
- Translate research into applications in emerging domains including digital twins, autonomous vehicles, augmented reality, edge computing, extreme data processing, computational science, real-time predictions.
- Initiate and lead collaborations with ECO members and within TU/e and with national and international academic and industrial partners, driving multidisciplinary projects and sector plan initiatives.
- Secure funding and disseminate results by writing competitive research proposals, acquiring public and private funding, and publishing internationally leading work in high-impact journals.
- Contribute to education and mentorship by developing and teaching courses in the Electrical Engineering Bachelor’s and Master’s programs, obtaining a University Teaching Qualification, and supervising BSc, MSc, EngD, and PhD students and PDs.
Your research will play a central role in the photonic systems activities at the TU/e
Casimir Institute, contributing to sustainable, high-performance communications and computing infrastructures. This inherently interdisciplinary work offers opportunities for collaboration, bridging fundamental advances with practical application demonstrators.
In this role, you will contribute to the ambition of the
Beethoven program to educate top talent and strengthen research across the fields of Science, Technology, Engineering, and Mathematics (STEM).