AI-Driven Multi-Agents for EDA Productivity and Autonomous DSE
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The Center for Industrial Electronics (CEIMM) at Universidad Politécnica de Madrid (UPM) is offering two PhD positions within a new regional research initiative focused on applying AI-driven methodologies to Electronic Design Automation (EDA). These positions align with a synergistic effort to combine multi-agent learning, generative AI, and Digital Twin frameworks to enhance the efficiency, accuracy, and sustainability of semiconductor digital design flows.
The selected PhD candidates will investigate and develop novel multi-agent AI systems that guide various stages of the EDA flow, from RTL generation and optimization to physical design and layout. Each agent will specialize in a specific stage of the flow and interact with a shared Digital Twin representation that captures real-time simulation feedback, including physical constraints such as thermal or power-aware models. The goal is to enable a closed-loop optimization framework that is adaptable, explainable, and efficient.
The research will involve integrating open-source tools such as CircuitOps and OpenROAD, and creating structured datasets for AI training, with fallback plans for dataset generation through simulation or synthetic augmentation. Candidates will explore reinforcement learning, LLMs, and hybrid AI approaches for adaptive optimization of EDA processes. Special attention will also be given to sustainability concerns, including the energy cost of AI models and the environmental impact of design flows.
Candidate Profile:
- MSc in Computer Engineering, Electronic Engineering, Embedded Systems, or related field
- Knowledge of HDL (VHDL/Verilog), logic synthesis, or EDA toolchains
- Programming experience in Python and ML frameworks (e.g., PyTorch, TensorFlow)
- Strong interest in AI for hardware design and digital twin technologies
- Familiarity with open-source EDA tools (Yosys, OpenROAD, Verilator)
- Knowledge of graph-based models, reinforcement learning, or LLMs
- Background in hardware design workflows, optimization, or thermal/electrical simulation
Your Job
The candidate will develop a hierarchical multi-agent AI framework, design adaptive control mechanisms for EDA flows, integrate and validate digital twin simulations, and prototype AI-driven feedback pipelines. The work will include collaborative research with academic and industrial partners, publication in high-impact venues, and the supervision of junior researchers. The candidate may also participate in teaching activities up to two semesters as a teaching assistant.
Salary and Conditions
BASIC QUALIFICATIONS: Master’s degree in Electrical /Electronics / Computer Engineering, Computer Science, Physics, or similar. Previous research activities in the fields of interest will be highly appreciated.
PREFERRED SKILLS: Very good knowledge of digital hardware design and SystemVerilog/VHDL; High-level synthesis tools for FPGAs; C/C++ programming for embedded systems. Experience with RISC-V processors is also valuable. A high level of English is required; Spanish is optional.
POSITION AVAILABLE: The selection procedure will remain open until the position is covered. The starting date is flexible, but we prefer it to be ASAP.
INFORMATION: If you are interested in this position and want to have more details about it, please contact Dr. José Miranda Calero ([email protected]) and Dr. Andrés Otero ([email protected]), including the reference [PhD_AI4EDA] in the subject of your mail. For the application, the following documents will be required:
- CV
- Academic transcript of courses followed and grades obtained provided by your institution.
- Brief letter describing your motivation and previous experience.
About
Universidad Politécnica de Madrid (UPM)
Also known as: UPM
The Technical University of Madrid (UPM) is Spain's largest technical university, offering diverse programs, fostering international collaborations, and excelling in research and innovation across multiple disciplines.
Summary
The Universidad Politécnica de Madrid offers two PhD positions in AI-driven methodologies for EDA, focusing on multi-agent systems to enhance semiconductor design efficiency and sustainability.