PhD on Neuromorphic-photonics-assisted front-end for edge computing – SpikeHERO project

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.

Job Requirements

• A master’s degree (or an equivalent university degree) in photonics, electro, applied physics or similar.
• Fluent in spoken and written English (C1 level).

Desirable Applicant Competencies

The candidate would ideally have a theoretical understanding and/or experimental experience in one or more of the following areas:

1. Photonic integration, computing and AI background.
2. Experimental and practical skills.
3. Python coding and simulation and design skills.
4. Curiosity, proactiveness, originality, commitment.

Conditions of Employment

A meaningful job in a dynamic and ambitious university, in an interdisciplinary setting and within an international network. You will work on a beautiful, green campus within walking distance of the central train station. In addition, we offer you:

• Full-time employment for four years, with an intermediate assessment after nine months. You will spend a minimum of 10% of your four-year employment on teaching tasks, with a maximum of 15% per year of your employment.
• Salary and benefits (such as a pension scheme, paid pregnancy and maternity leave, partially paid parental leave) in accordance with the Collective Labour Agreement for Dutch Universities, scale P (min. € 2,901 max. € 3,707).
• A year-end bonus of 8.3% and annual vacation pay of 8%.
• High-quality training programs and other support to grow into a self-aware, autonomous scientific researcher. At TU/e we challenge you to take charge of your own learning process.
• An excellent technical infrastructure, on-campus children’s day care and sports facilities.
• An allowance for commuting, working from home and internet costs.
• A Staff Immigration Team and a tax compensation scheme (the 30% facility) for international candidates.

Information

Do you recognize yourself in this profile and would you like to know more? Please contact the hiring manager R. Stabile, Associate Professor (r.stabile@tue.nl) and prof. Chigo Okonkwo (cokonkow@tue.nl).

Visit our website for more information about the application process or the conditions of employment. You can also contact HR Services (HRServices.flux@tue.nl).

Are you inspired and would like to know more about working at TU/e? Please visit our career page.

Application

We invite you to submit a complete application by using the apply button. The application should include a:

• Cover letter in which you describe your motivation and qualifications for the position.
• Curriculum vitae, including a list of your publications and the contact information of three references.
• A written scientific report in English (MSc thesis, traineeship report or scientific paper).

We look forward to receiving your application and will screen it as soon as possible. The vacancy will remain open until the position is filled.

Reference number: 2025/306