The Embedded Systems unit of EAVISE at KU Leuven is looking for a highly motivated PhD researcher to join a novel research initiative investigating how embedded AI systems can dynamically optimize themselves after deployment. EAVISE is a multidisciplinary research group developing state-of-the-art AI, embedded computing, computer vision, and edge intelligence solutions for real-world applications. The Embedded Systems unit focuses on hardware-software co-design, AI deployment tools, and energy-efficient computing for future intelligent devices.
Project
Modern embedded AI systems rely on Deep Neural Networks (DNNs) running on resource-constrained devices such as wearables, smart sensors, hearables, and IoT nodes. While current deployment methodologies can optimize models before deployment, the resulting software remains static throughout the lifetime of the device. Consequently, energy consumption, memory usage, and computational requirements remain fixed, even when the application or environmental conditions evolve over time.
This project investigates a radically new paradigm for Embedded AI: enabling devices to dynamically compress and adapt neural networks directly on-device after deployment. Inspired by how humans continuously optimize their physical efficiency through training, this project aims to develop software methods that allow deployed neural networks to progressively reduce their resource footprint while maintaining application performance.
The research focuses include: runtime-reconfigurable neural network software kernels for novel embedded systems architecture, performance models for adaptive AI workloads, on-device search and optimization engines for memory-aware deployment, compiler and runtime systems capable of supporting dynamic neural network topologies, energy-efficient embedded AI frameworks targeting RISC-V multicore and heterogeneous computing platforms.
The project combines fundamental research in embedded software and compiler systems with practical validation on state-of-the-art embedded AI hardware platforms. Successful outcomes could significantly extend battery lifetime and enable a new generation of adaptive and sustainable embedded AI systems
Profile
We are looking for highly motivated PhD researchers with a strong interest in hardware-software design for embedded systems and Embedded AI.
- Candidates must hold a master’s degree in electrical or computer engineering (or equivalent). Master's students who expect to graduate by the end of the year are also encouraged to apply.
- Programming experience (C/C++) within the embedded field and/or software engineering and compilers is highly recommended.
- Strong background in computer architectures and embedded platforms (ARM Cortex-M, NPU, FPGA, embedded GPU), e.g., via academic courses and/or project courses
- Research experience (e.g., through a Master thesis work or research internships) is considered a strong asset.
- Experience with Deep Learning and Artificial Intelligence is considered a plus.
- Excellent proficiency in the English language is required, as well as good communication skills, both oral and written.
- Strong interpersonal skills and the ability to work in an international team.
Offer
- A PhD scholarship for up to 4 years (subject to positive intermediate evaluations).
- An exciting research environment, working on the intersection between theory and implementation in a very multi-disciplinary research environment.
- A thorough scientific education in the frame of a doctoral training program, with the possibility of becoming a world-class researcher.
- A competitive salary or tax-free PhD grant, including the reimbursement of commute costs to the workplace.
- Flexible working conditions, upon agreement with the PhD supervisor
- The possibility to participate in international conferences, workshops, and collaborations with top EU and KU Leuven research teams.
Interested?
In addition to the documents required by the application system, a complete application must include:
- A summary (maximum 4 pages) of the Master's thesis dissertation (or, for candidates who have not yet graduated, a summary of the ongoing thesis work).
- A summary (maximum 3 pages) of relevant technical projects carried out during academic studies, internships, work experience, or personal projects that are related to the scope of the PhD position.
- Please ensure that your CV clearly indicates the duration of your studies (and the expected graduation date, if applicable) and includes up to three references who may be contacted regarding your qualifications and application.
Only applications submitted through the official application channel will be considered.
For more information please contact Prof. dr. ir. Manuele Rusci, mail:
[email protected].
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