[School of Engineering PhD Scholarships] Autonomous Layout Synthesis for High-Density Power Electronics PCBs

About the project

About the Project Power electronics is the technology behind converting and controlling electrical energy — stepping voltages up or down, switching between AC and DC, and regulating power flow using fast switching devices rather than bulky transformers and resistors. It is arguably the most widely built type of circuit in the world, because almost every piece of electronics needs some form of energy conversion inside it: the adapter that charges your phone, the drive that powers an electric vehicle's motor and manages its fast charging, the inverters that turn variable solar and wind power into grid-ready electricity, the high-voltage DC links and substations that move power across national grids, and the conversion stages inside every data centre that bring mains voltage down to what server chips need. Despite how widespread these circuits are, designing their physical PCB layout remains a largely manual, expertise-bound craft. Where components are placed and how they are connected directly affects parasitic inductance, heat dissipation, electromagnetic interference, and safety clearances — yet these decisions are still made by hand, using rules of thumb built up over years of experience. As converter designs grow more complex and development timelines shrink, this manual process is becoming a genuine bottleneck for the power electronics industry. This PhD project will tackle that bottleneck by developing an AI-driven placement and routing engine specifically for power electronics PCBs, capable of automatically generating layouts that meet electrical and thermal performance requirements without needing an expert designer in the loop. The work builds on an existing reinforcement-learning and Monte Carlo Tree Search pipeline combined with a fractal placement framework, and will involve teaching the system to understand power-specific design rules — minimising stray inductance around switching nodes, managing heat flow, and respecting safety clearances — starting with simple converter circuits and scaling up to realistic multi-device power stages. This is a highly interdisciplinary project, combining power electronics, machine learning, computational geometry, and software engineering, and is well suited to a student who enjoys building real systems as much as developing theory. The successful candidate will gain hands-on experience with reinforcement learning, GPU-accelerated computing, circuit simulation, and PCB design, alongside a solid grounding in power converter fundamentals. There is also a clear route to publishing in leading power electronics venues, and potential for the tools developed to have real-world impact beyond academia. The student will join the Power Electronics, Machines and Drives group, one of the largest and most active power electronics research groups in the UK, with weekly supervision, dedicated GPU compute resources, and access to an established codebase to build from. Structured training will be provided in reinforcement learning methods, power converter design, PCB CAD tools, and research and software engineering skills, alongside opportunities to present work at internal seminars and international conferences from the first year onward. This project is ideal for a candidate with a background in electrical or electronic engineering, computer science, or a related field, who is curious about applying modern AI techniques to a genuinely unsolved engineering problem with wide industrial relevance. This project is expected to start in September 2027. Before you apply: We strongly recommend that you contact the supervisors for this project before you apply. How to apply: To be considered for this project you must complete a formal application through our online application portal. If you already have an applicant account this link will directly open an application for PhD School of Engineering Scholarships . If you don’t already have an applicant account, please follow the instructions here . When applying, please specify the full title and supervisor/s of the project, details of your previous study, and names and contact details of two referees. You must also upload a Supporting Statement describing the motivation to apply to the project, your CV and transcripts of awarded and in-progress university qualifications . Please note late or incomplete applications will not be considered. Equality, diversity and inclusion are fundamental to the success of The University of Manchester and central to all our activities. A diverse research community strengthens creativity, productivity and quality, while increasing the societal and economic impact of our work. We welcome applicants from all career paths, backgrounds and sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation or transgender status. We welcome applications from candidates returning to study after a career break or experience in other roles. Flexible study arrangements may be available, including part-time study at 50%, 60% or 80%, subject to the requirements of the project and funder. Eligibility : The standard academic entry requirement for this PhD is an upper second-class (2:1) honours degree in Electrical and Electronic Engineering, Computer Science or a discipline directly relevant to the PhD (or international equivalent) OR any upper-second class (2:1) honours degree and a Master’s degree at merit in Electrical and Electronic Engineering, Computer Science or a discipline directly relevant to the PhD (or international equivalent). Good programming skills (Python and/or C++) and familiarity with circuit fundamentals are required. Previous research experience in power electronics, PCB design, or machine learning is desirable. This project will remain open until filled. If your application is submitted by 1 st November 2026, you can expect a decision by 18 th December 2026. If your application is submitted by 15 th January 2027, you can expect a decision by 30 th March 2027. Self or externally funded students can also be considered for this project. FSESoE

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