Job description
The Photonic Neural Network Lab, together with the High Capacity Optical Transmission Lab’s in the Electro-Optics Communication (ECO) group at Eindhoven University of Technology (TU/e) are recruiting a PhD candidate to research novel neuromorphic-photonics-assisted front-ends for edge computing.
The electro-optical communications (ECO) group in the Faculty of Electrical Engineering at TU/e is a globally recognised, leading scientific and applied research group focused on exploiting light for communication and quantum systems. We apply our knowledge in collaboration with other scientists at TU/e and more recently within the newly formed Eindhoven Hendrik Casimir Institute (EHCI) to develop the required solution for many of the relevant challenges in communication and sensing systems. The group expertise spans from the fundamentals and physics of photonics, optics, the design and fabrication of photonic integrated circuits (PICs), systems engineering to exploiting optical linear/non-linear signal processing to unlock fiber capacity and relevant higher layer protocols required to operate modern optical communication and quantum networks.
Based in the purposely built FLUX building at the TU/e Campus, the ECO group has access to 300m2 of labs for conducting experimental research and is supported by a state-of-the-art 800m2 cleanroom. With greater than 100 group members including 13 tenured scientists, 79 PhD candidates, 16 postdocs and senior researchers, the ECO group is a vibrant and exciting research group perfectly suited for talented and ambitious scientists. The group is active in spin outs and starts-ups (e.g. CubiQ, Micro-align, PhotonX Networks and LuXisens Technology) and carries out bilateral industrial research with major stakeholders in the communications industry.
Information
As the demand for edge computing continues to rise, there is a growing need for systems that can process data locally with minimal delay and reduced energy consumption. Traditional computing architectures—rooted in CMOS technology and the von Neumann model—are increasingly unable to meet these emerging requirements. Their fundamental design, which separates memory and processing units, creates inefficiencies such as data transfer bottlenecks and higher power usage.
SpikeHERO is a European research initiative funded under the 2024 EIC Pathfinder Challenges, within the programme focused on nanoelectronics for energy-efficient smart edge devices. This project brings together leading academic institutions and innovative companies from across Europe in a collaborative effort to address the pressing challenges of energy consumption and performance limitations in today’s edge devices.
The project—Spiking Hybrid Edge computing for Robust Optoelectronical signal processing—introduces a novel approach that combines three key technologies: event-driven Spiking Neural Networks (SNNs) designed for ultra-low-power operation; optoelectronic signal processing, which integrates optical and electrical components for faster and more efficient data handling; and advanced hardware integration techniques, including 3D heterogeneous integration and beyond-CMOS materials. At its core, SpikeHERO proposes a new class of hybrid neural systems that combine optical and electrical SNNs to significantly enhance processing speed and energy efficiency, setting a new standard for edge computing performance.
In this context, the candidate will join the ECO group and work also closely with the Photonic Integration (PhI) group, responsible of the development of a semiconductor technology platform using mature indium phosphide (InP) photonic circuits. The PhD student will aim to implement optical neural networks (ONNs), with a clear pathway to interface them with electronic and photonic spiking neural networks (SNN), for reconfigurable hybrid ONN/SNN architectures. This platform will also integrate high-speed optical transceivers, including tunable lasers, modulators, and detector arrays. The result is a pioneering optical front-end capable of both ultra-fast communication and advanced signal processing.
The candidate will design and simulate ONNs to perform filtering, chromatic dispersion compensation and multi-tap equalization, supporting data integrity at the edge. The candidate’s responsibilities will include modelling, designing, and taping out of neural network topologies for specific tasks like signal denoising and impairment mitigation. This includes exploring ONN-based digital filtering for high-speed data, developing techniques to reduce post-processing workload, and applying shallow neural architectures to lower signal dimensionality. Additionally, the work will support the development of ONN-base interfaces to Spiking Communication Protocol. Ultimately, he will validate the hybrid neural system developed within SpikeHERO will be validated through a key use case focused on Fiber-To-The-Edge (FTTE) applications, addressing the low-latency and high-speed processing demands of future 6G networks. This demonstration will highlight the system's potential not only for next-generation wireless networks but also for broader applications such as autonomous vehicles and smart infrastructure, offering a roadmap for scalable, energy-efficient edge intelligence.