We are looking for highly motivated PhD researchers with a strong interest in hardware-software design for embedded systems.
1. Candidates must hold a master’s degree in electrical or computer engineering (or equivalent).
2. Programming experience (C/C++) within the embedded field and/or software engineering and compilers is highly recommended.
3. Strong background in computer architectures and embedded platforms (ARM Cortex-M, NPU, FPGA, embedded GPU), e.g., via academic courses and/or project courses
4. Research experience (e.g., through a Master thesis work or research internships) is considered a strong asset.
5. Experience with Deep Learning and Artificial Intelligence is considered a plus.
6. Excellent proficiency in the English language is required, as well as good communication skills, both oral and written.
7. Strong interpersonal skills and the ability to work in an international team.
In recent years, hardware architectures for embedded AI (particularly in the area of Deep Learning) have become extremely modular and heterogeneous, while retaining high flexibility through software programming to serve a wide range of AI-based applications. Because of the complexity of these designs, it is nowadays common to rely on end-to-end software compilation toolchains, generally denoted as AI compilers, to automate the deployment of AI workloads on the target device. The present software and tool approaches fail, however, to support innovative multi-accelerator and memory-constrained platforms, without heavily relying on human interventions for hardware-aware optimization, or are unable to generate runtime-adaptive workloads, such as AI algorithms that evolve over time in response to external environments or personalized scenarios.
This research project investigates new software-based acceleration solutions and AI compiler toolchains for efficiently running AI-based tasks, such as Deep Learning inference and training, on cutting-edge heterogeneous processing systems. This can involve researching novel hardware-aware software acceleration techniques, such as targeting RISC-V-based processors or specialized tensor accelerators, or hardware-software co-design of new architectural solutions. Other areas of research include AI compiler toolchains for adaptive models and on-device learning, as well as software tools like system simulators for end-to-end testing and validation. Over time, the project is expected to shift from basic research to more applied uses, such as wearable devices like smart glasses and hearables, and autonomous driving applications.
EAVISE is a multidisciplinary KU Leuven research group based at Campus De Nayer in Sint-Katelijne-Waver. The research goal of EAVISE is to apply state-of-the-art AI, computer vision, speech processing, and artificial intelligence as a solution for industry-specific and real-world problems. To meet stringent execution speed, energy footprint, cost, and price requirements, the developed AI algorithms are often implemented and optimized on embedded systems, such as embedded GPUs, DSPs, FPGAs, or specialized ICs. Application areas include industrial automation, autonomous driving, product inspection, traffic monitoring, e-health, agriculture, eye-tracking, microscopic imaging, surveillance, and land surveying. This PhD opening is located within the Embedded System unit of EAVISE, which specializes in hardware-software design of future processing systems for energy-efficient embedded AI.
8. A PhD scholarship for up to 4 years (subject to positive intermediate evaluations).
9. An exciting research environment, working on the intersection between theory and implementation in a very multi-disciplinary research environment.
10. A PhD title from a highly ranked university (#1 in Europe in terms of scientific innovation) after approximately 4 years of successful research.
11. A thorough scientific education in the frame of a doctoral training program, with the possibility of becoming a world-class researcher.
12. A competitive salary or tax-free PhD grant, including the reimbursement of commute costs to the workplace (Campus De Nayer).
13. Flexible working conditions, upon agreement with the PhD supervisor
14. The possibility to participate in international conferences, workshops, and collaborations with top EU and KU Leuven research teams.