SHAPE THE FUTURE OF AI HARDWARE FOR WEARABLE AND IMPLANTABLE DEVICES

Deploying AI on resource-constrained platforms such as wearable and implantable devices opens exciting possibilities to improve healthcare, enhance quality of life, and contribute to more sustainable computing. Achieving this requires radically new, energy-efficient computing engines built on innovative architectures and emerging device technologies. In this aspect, Emerging computation-in-memory (CIM) architectures, particularly those using memristor-based technologies, offer a promising potential for energy-efficient AI acceleration by performing computational tasks directly within the memory. In CIM, the storage unit consists of a highly compact crossbar structure built using non-volatile, scalable, and CMOS-compatible memristor devices, such as resistive random-access memory (RRAM). The data stored in memristor devices is analog, represented as resistance states, which enables data access in the analog domain. This approach not only facilitates greater parallelism but also improves the scalability of operations, resulting in significant performance gains for data-intensive tasks.
The Computer Engineering (CE) section of the Quantum & Computer Engineering (QCE) department is looking for a highly motivated PhD candidate to join our research team working on Analog In-Memory Brain-Inspired AI Accelerators. Your research will directly contribute to the development of AI hardware capable of processing complex tasks efficiently at the edge — where energy is limited, but impact is high.
You’ll be part of a diverse and passionate team of academic staff, PhD candidates, and postdocs in the Computer Engineering Section. We value open discussions, sharing ideas, and collaborating regularly to advance our understanding of computer engineering. You’ll also receive comprehensive training to support your growth as a scientist.

YOUR PROFILE

We are looking for a candidate who brings curiosity, creativity, and technical excellence to the project. You ideally have:

• A completed MSc degree in Electrical Engineering, Computer Engineering, or a related field;
• Solid knowledge of analog and/or digital circuit design;
• Basic understanding of AI algorithms and hardware implementations (a plus);
• Interest in teaching and mentoring students;
• Strong collaboration skills and the ability to take initiative.
• You meet the eligibility criteria of the MSCA programme.

While being an integral part of the team, you will work at the intersection of AI hardware, emerging memory technologies, and analog circuit design:

• Design and develop Computation-In-Memory (CIM)-based architectures using memristive devices.
• Implement, evaluate and refine the architectures.
• Demonstrate the potential of the architectures through tape-outs.

You will also have the opportunity to:

• Collaborate closely with international partners within the European MSCA Doctoral Network TIRAMISU – Training and Innovation in Reliable and Efficient Chip Design for Edge AI (2024-2028).
• Strengthen your academic and teaching skills by guiding MSc students and contributing to the educational program.
• Present your findings with the global scientific community by presenting at leading international conferences and publishing in top journals.

This position is a part of a new European MSCA Doctoral Network TIRAMISU “Training and Innovation in Reliable and Efficient Chip Design for Edge AI” (2024-2028) – an EU-funded program designed to train future European engineers and researchers driving innovation in reliable and energy-efficient Edge AI chips. The program provides strong interdisciplinary training, international collaboration, and mobility opportunities. Learn more at https://tiramisu-project.eu/.