PhD position in hardware-software co-design for bio-inspired ML models

KU Leuven; Vlaams-Brabant; Hybride; EUR 26 000 - 36 000; Plein temps

Avantages offerts par ce poste

PhD scholarship 4 years

Competitive salary

Health insurance

International research environment

Up to 40% remote work

Résumé du poste

KU Leuven's e-Media research lab invites applications for a PhD position in hardware-software co-design for bio-inspired ML models. You will join Prof.

Martin Lefebvre's team within STADIUS/ESAT to develop energy-efficient edge ML on FPGA/ASIC, combining training of spiking neural networks with novel plasticities. The role requires a MSc in engineering with top grades, strong ML and Python skills, and experience with FPGA/RISC-V and EDA tools.

Qualifications

• Master's degree in Engineering with top performance.
• Top 10% MSc and BSc with outstanding results.
• Strong background in ML algorithms and Python.
• Experience with embedded platforms (FPGA/RISC‑V) and EDA tools such as Cadence XCelium, Genus, Innovus.

Responsabilités

• Develop hardware-software co-design for bio-inspired ML models.
• Explore training spiking neural networks with multiple plasticities.
• Quantize models and implement on FPGA/ASIC hardware.
• Collaborate in an international team and disseminate results.

Connaissances

Python

PyTorch

JAX

ML algorithms

English proficiency

Research collaboration

Team communication

Formation

Master's degree in Engineering

Top 10% MSc & BSc

Outils

Cadence XCelium

Genus

Innovus

Description du poste

PhD position in hardware-software co-design for bio-inspired ML models

(ref. BAP-2026-482)
Laatst aangepast: 21/07/26

The PhD researcher will join the e-Media research lab under the supervision of Prof. Martin Lefebvre, whose research focuses on hardware-software co-design of machine learning (ML) models that take inspiration from the brain, to enable learning and efficient processing at the edge. More broadly, the e-Media research lab covers research topics spanning from signal processing and data analysis to machine learning and human-computer interaction. It is part of the Center for Dynamical Systems, Signal Processing, and Data Analytics (STADIUS) and of the Department of Electrical Engineering (ESAT) at KU Leuven.

Website van de eenheid

Project

In the last decade, ML models taking inspiration from the brain and biological neurons have emerged as an alternative to conventional ML models, promising a more efficient processing of temporal signals at the edge. This includes a wide range of signals, among which cortical activity for brain-computer interfaces, audio signals for keyword spotting and artificial cochleas, and tactile signals for robot perception. Various types of bio-inspired mechanisms have been investigated in recent years to endow these bio-inspired models with efficient processing capabilities. Structural plasticity for example consists in letting the network’s sparse connectivity evolve throughout training, while delay plasticity consists in learning dendritic, synaptic, or axonal temporal delays to enrich the network’s spatiotemporal dynamics. This research project will thus explore how these different mechanisms can be optimally combined to deliver models with extremely constrained compute and memory footprints without compromising performance. This includes training spiking neural networks with multiple plasticities at different timescales, exploring trade-offs between different design choices, quantizing the models, and implementing the best-performing ones in custom digital hardware, on FPGA and/or in an application‑specific integrated circuit. These results will pave the way for on‑device edge intelligence, as well as for a better understanding of relevant bio‑inspired mechanisms to be included in resource‑constrained models at the edge. While biomedical applications will be the primary focus of this work, the resulting ML models and hardware will be designed to be easily portable to a broad range of tasks based on temporal data (predictive maintenance, sensory processing for robot control, etc), in which efficient on‑device processing is crucial.

Profile

• have a Master’s degree in Engineering with a background in Electrical Engineering, Computer Science, Artificial Intelligence, or a related field
• be ranked within the top 10% of their class at MSc and BSc level, with outstanding study results
• have a strong background in ML algorithms, with programming experience in Python and, more specifically, in common deep learning frameworks such as PyTorch and jax, for model training and inference
• have experience with embedded platforms such as FPGAs or RISC-V microcontrollers. Experience with digital integrated circuit (IC) simulation and design tools, such as Cadence XCelium, Genus, and Innovus, is a plus
• have qualities to carry out independent research, among which communication skills, critical thinking, and research ethics
• demonstrate an excellent command of the English language, both in spoken and written form
• show strong interpersonal skills and the ability to work in an international team

Offer

• A PhD scholarship of up to four years and, if successful, a PhD in Engineering Technology
• A competitive salary and additional benefits such as health insurance, access to university sports facilities, etc
• The opportunity to be active in an exciting and international research environment, engage in research collaborations, and participate to international conferences
• Full‑time employment for four years, with an intermediate evaluation after each year
• Excellent doctoral training at the Arenberg Doctoral School in an international environment at a top European university
• A flexible working culture with opportunity for up to 40% remote working

https://iiw.kuleuven.be/onderzoek/emedia

KU Leuven strives for an inclusive, respectful and socially safe environment. We embrace diversity among individuals and groups as an asset. Open dialogue and differences in perspective are essential for an ambitious research and educational environment. In our commitment to equal opportunity, we recognize the consequences of historical inequalities. We do not accept any form of discrimination based on, but not limited to, gender identity and expression, sexual orientation, age, ethnic or national background, skin colour, religious and philosophical diversity, neurodivergence, employment disability, health, or socioeconomic status. For questions about accessibility or support offered, we are happy to assist you at this email address.