This PhD opportunity at the University of Southampton focuses on developing ferroelectric diodes (FeDs) with two-dimensional (2D) semiconductors for next-generation artificial intelligence (AI) hardware. The project aims to address critical energy and latency bottlenecks in modern computing by integrating memory and computation, paving the way for neuromorphic devices that are both efficient and scalable. Ferroelectric diodes are non-volatile memory devices exhibiting rectifying current–voltage hysteresis, making them ideal for dense crossbar arrays in neuromorphic computing architectures. These architectures are designed to overcome the energy, latency, and data-movement limitations of current AI hardware. Despite their promise, FeDs face challenges such as limited cyclability, high operating voltages, insufficient read currents, and device variability, which restrict their scalability and commercial viability. Building on recent breakthroughs in ultra-clean metal contacts to 2D semiconductors and ferroelectrics, this project seeks to develop reliable, multi-bit ferroelectric diode arrays using foundry-compatible processes. The research will focus on controlling ferroelectric domain structure, switching dynamics, and interfacial electrostatics to achieve stable, analog-like conductance states suitable for neuromorphic learning and inference. By systematically addressing endurance, variability, and operating voltage, the project will establish scalable design principles aligned with the requirements of emerging AI hardware. The successful candidate will join the Materials for Intelligent Nanoelectronic Devices (MINDs) lab and receive comprehensive training in nanofabrication and materials characterisation at the Southampton Nanofabrication Centre, one of Europe's most advanced university cleanrooms. Device development will be complemented by advanced electronic and optoelectronic characterisation within the Sustainable Electronic Technologies (SET) group and the ECS Centre for Neuromorphic Technologies. The project also involves collaboration with the University of Cambridge and the National Physical Laboratory (NPL), providing access to world-class metrology and device benchmarking expertise. This interdisciplinary environment is designed to prepare students for careers in academic research and the rapidly growing AI hardware industry. The School of Electronics & Computer Science is committed to equality, diversity, and inclusivity, welcoming applicants from all backgrounds and offering flexible working patterns, generous maternity policies, and onsite childcare facilities. Eligibility: Applicants must hold a UK 2:1 honours degree or its international equivalent in electronics, materials science, physics, or a related discipline. Essential skills include hands-on project experience, nanomaterials synthesis and characterisation, coding or simulation experience (MATLAB, COMSOL, Python), and knowledge of 2D materials, semiconductors, and ferroelectrics. English language qualification is required if applicable. Funding: The University offers a range of funding opportunities for both UK and international students, including Horizon Europe fee waivers, competition-based Presidential Bursaries, and studentships covering tuition fees and a stipend for living costs. Funding is awarded on a rolling basis, so early application is recommended. Application Process: Apply online by selecting the PhD Electronic & Electrical Engineering (7092) programme (Research, 2026/27, Faculty of Engineering and Physical Sciences), choosing full-time or part-time. Include the supervisor's name in section 2 of the application. Applications should comprise a research proposal, CV, two academic references, degree transcripts and certificates, and English language qualification if applicable. For general queries, contact [email protected]. For project-specific questions, email Dr Soumya Sarkar ([email protected]). For further details and to apply, visit the FindAPhD project page .