PhD Position On-chip Photonic Neural Network

AI computing with light! Come join us to design and fabricate a unique photonic AI chip that can learn and perform tasks at light speed and high energy efficiency.

Job description

Photonic neural networks offer a promising route toward overcoming the energy and bandwidth limitations of conventional electronic AI hardware. By exploiting interference, parallelism, and the intrinsic speed of light, integrated photonics can execute matrix operations with unprecedented efficiency. This PhD project focuses on developing on‑chip photonic neural network architectures that can be trained directly in hardware, eliminating the need for costly off‑chip training and enabling scalable, adaptive systems.

You will work at the intersection of nanophotonics, machine learning, and hardware‑software co‑design to realize robust training mechanisms within programmable photonic circuits. The research combines theoretical modeling, chip design, experimental validation, and algorithm development, contributing to a new generation of sustainable, high-performance AI chips for real‑world applications.

Your role

• Design and model photonic neural network architectures for on‑chip implementation.
• Develop in‑situ training strategies compatible with analog photonic hardware.
• Fabricate and experimentally characterize novel photonic AI chips.
• Collaborate with interdisciplinary teams spanning nanophotonics and AI.
• Disseminate results through scientific publications, conference presentations, and patents.

What we offer

• A 4‑year fully funded PhD position at TU Delft.
• The opportunity to lead a pioneering project at the intersection of nanophotonics and AI.
• Collaboration with international experts from diverse disciplines.
• Access to cutting‑edge computational and experimental facilities.
• Supervision of MSc researchers.
• Mentorship and career development support.

This position is jointly embedded in the research groups of Dr. Sid Kumar and Dr. Richard Norte. It involves working across two departments, with shared responsibilities and collaboration.

Job requirements

• Master’s degree in physics, applied physics, nanophotonics, computer science, artificial intelligence, mechanical engineering or a related field.
• Experience in nanofabrication.
• Experience with machine learning for scientific applications.
• Experience with programming (e.g. Python, PyTorch, TensorFlow).
• Drive to tackle interdisciplinary challenges and communicate across fields.
• Strong teamwork and communication skills.

Conditions of employment

Doctoral candidates will be offered a 4‑year period of employment in principle, but in the form of 2 employment contracts. An initial 1.5‑year contract with an official go/no‑go progress assessment within 15 months is followed by an additional contract for the remaining 2.5 years, assuming everything goes well and performance requirements are met.

Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities, increasing from €3,059 – €3,881 gross per month, from the first year to the fourth year based on a full‑time contract (38 hours), plus 8 % holiday allowance and an end‑of‑year bonus of 8.3 %. As a PhD candidate you will be enrolled in the TU Delft Graduate School.

For those who need to relocate to the Netherlands, TU Delft offers support through its Coming to Delft Service, which provides information, events, and a Dual Career Programme to assist partners.

Additional information

For more information about this vacancy, please contact Sid Kumar at Sid.Kumar@tudelft.nl and Richard Norte at R.A.Norte@tudelft.nl.

About EEO

At TU Delft we embrace diversity as one of our core values and actively engage to be a university where you feel at home and can flourish. We value different perspectives and qualities, making our work more innovative, the TU Delft community more vibrant and the world more just.

Application requirements

Applicants must demonstrate English proficiency sufficient to participate in English‑taught Doctoral Education courses, write scientific articles, and complete the final thesis.

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