Machine Learning — PhD Intern (Benchmarking for Inverse Design and Optimization)
Keysight · Posted Nov 5, 2026
- Barcelona, Barcelona, Spain
- Internship · Intern
This internship focuses on rigorous benchmarking of machine learning and optimization methods for inverse design and optimization. You will develop a reproducible benchmarking framework for comparing an existing engineering design workflow with selected academic and open-source methods across representative inverse design and optimization problems.
Responsibilities
- Review relevant state-of-the-art research and available open-source implementations in inverse design, black-box optimization, reinforcement learning, and related methods as appropriate to the selected use cases.
- Select a small set of technically meaningful baseline methods together with the supervising researchers.
- Define representative engineering optimization problems with explicit design variables, objectives, constraints, parameter ranges, and simulation settings.
- Establish matched experimental protocols covering initialization, random seeds, evaluation budgets, stopping criteria, and training or model-building costs.
- Adapt and integrate baseline implementations with existing engineering simulation workflows.
Requirements
- PhD currently pursuing or recently completed in Machine Learning, Computer Science, Electrical or Electronic Engineering, Applied Mathematics, or a related technical field
- Nice to have: sample efficiency
- Nice to have: transfer learning
- Nice to have: inverse design
- Nice to have: RF engineering