"PhD Position In-Memory AI Accelerators for Energy-Efficient Edge Computing"

Job Summary

Deploying AI on resource-constrained platforms such as wearable and implantable devices aims to improve healthcare, enhance quality of life, and promote sustainable computing. This involves developing energy-efficient computing engines using emerging computation-in-memory (CIM) architectures with memristor-based technologies for AI acceleration.

Responsibilities

• 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.
• Collaborate with international partners in the European MSCA Doctoral Network TIRAMISU.
• Strengthen academic and teaching skills by guiding MSc students and contributing to educational programs.
• Present findings at international conferences and publish in top journals.

Qualifications and Requirements

• 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 (preferred).
• Interest in teaching and mentoring students.
• Strong collaboration skills and ability to take initiative.
• Meet the eligibility criteria of the MSCA programme.

What the Employer Offers

• A dynamic and inclusive research team in a collaborative academic setting.
• Close supervision and mentorship for 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.
• Opportunities to contribute to real-world applications in healthcare, sustainability, and AI technologies.
• Comprehensive training and skill development in analog design, emerging AI hardware, and interdisciplinary research.