Industrial PhD in Neuromorphic Computing, Spiking Neural Networks, and Hardware-Agnostic Compilers Universidad Politécnica de Madrid in Spain

This fully funded Industrial PhD position at Universidad Politécnica de Madrid (UPM), in partnership with Intera Group, focuses on advancing neuromorphic computing and spiking neural networks (SNNs). The research aims to develop a hardware-agnostic compiler and intermediate representation (IR) for efficient, portable deployment of SNNs across heterogeneous neuromorphic platforms, including FPGAs, ASICs, and CGRAs. The project integrates machine learning, compiler design, and hardware acceleration, contributing to the next generation of energy-efficient, brain-inspired computing systems.

Key research areas include the definition of neuromorphic intermediate representations, compiler passes for neuron model transformations, optimization and scheduling for event-driven systems, and spike routing. The work involves integration with Python-based neuromorphic frameworks and FPGA prototyping of neuromorphic accelerators. Validation and benchmarking will target embedded and digital-health applications, with objectives such as lowering standard neuromorphic IR, hardware back-end mapping, benchmarking SNN implementations, and developing complete neuromorphic processing pipelines.

The ideal candidate will have a Master’s degree in Data Science, Machine Learning, Mathematics, Electrical/Electronic Engineering, or Computer Engineering. Skills in ML, compilers, embedded systems, Python/C++, or FPGA design are highly valued. The position offers joint supervision between academia and industry, with flexible location options in Madrid, Barcelona, or both, and strong exposure to real industrial challenges and cutting-edge research.

Application involves reaching out via LinkedIn or email, sharing the opportunity, and preparing relevant documents. The position is fully funded, covering stipend and research expenses, and provides a unique opportunity to work on impactful technology in neuromorphic computing, AI at the edge, and hardware–software co-design.