PhD Analog In-Memory AI Accelerators for Energy-Efficient Edge Computing

Are you ready to unlock the potential of emerging technologies and architectures in designing the next generation smart wearable and implantable devices?

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 role as a PhD candidate

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.

What We Offer

You will be part of an inspiring, international research environment where collaboration, diversity, and personal growth are highly valued. We offer you:

• A dynamic and inclusive research team within a collaborative academic setting;
• Close supervision and mentorship to support both your technical and personal development;
• Access to state-of-the-art facilities for circuit design, fabrication, and testing;
• Strong connections with industrial partners and European academic leaders in the field of energy-efficient AI hardware;
• The opportunity to contribute to real-world applications in healthcare, sustainability, and next-generation AI technologies.

Throughout the project, you will develop a unique skill set in analog design, emerging AI hardware, and interdisciplinary research — preparing you for a successful career in academia or industry.

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.