Overview:

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Keysight is at the forefront of technology innovation, delivering breakthroughs and trusted insights in electronic design, simulation, prototyping, test, manufacturing, and optimization. Our ~16,800 employees create world-class solutions in communications, 5G, automotive, energy, quantum, aerospace, defense, and semiconductor markets for customers in over 100 countries. Learn more about what we do.

Our award-winning culture embraces a bold vision of where technology can take us and a passion for tackling challenging problems with industry-first solutions. We believe that when people feel a sense of belonging, they can be more creative, innovative, and thrive at all points in their careers.

About the Program

Keysight's AI team develops machine learning and optimization methods for engineering problems where simulation, measurement, and intelligent design workflows interact.

This internship focuses on rigorous benchmarking of machine learning and optimization methods for inverse design and optimization. Meaningful comparisons require more than running different algorithms on the same problem: objectives, constraints, initialization, evaluation budgets, simulation settings, and statistical procedures must be controlled carefully.

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.

Role Overview

As a PhD Intern in Machine Learning and Optimization, you will design, implement,



and evaluate a benchmark suite covering a small number of representative engineering design problems.

The work spans the full benchmarking process: reviewing relevant research, selecting strong comparison methods, defining fair experimental protocols, integrating available implementations, automating simulation-based experiments, and analyzing where different approaches succeed or fail.

A central objective will be to distinguish meaningful algorithmic differences from effects caused by evaluation budget, initialization, training cost, randomness, or implementation choices. You will measure both the quality of the final engineering solution and the computational or simulation effort required to obtain it.

The internship will focus on two agreed engineering use cases and approximately two or three carefully selected baseline methods. The goal is depth and experimental rigor rather than a broad but shallow algorithm survey.

By the end of the internship, you will deliver a reproducible benchmarking workflow, quantitative comparison results, and an evidence-based technical report describing performance, robustness, limitations, and areas requiring further evaluation.

You will work alongside machine learning researchers, optimization experts, and simulation engineers.

Learning Outcomes

- Apply modern optimization and machine learning methods to realistic engineering design problems.




- Learn how to design statistically sound benchmarks for simulation-based optimization and inverse design.
- Develop practical understanding of sample efficiency, robustness, convergence behavior, and solution quality.
- Learn how evaluation budgets and experimental protocol choices can materially affect conclusions about algorithm performance.
- Gain experience assessing research methods for reproducibility, applicability, and engineering relevance.
- Build working fluency with simulation-driven optimization and engineering design workflows.
- Develop experience automating large experimental studies and communicating uncertainty and limitations clearly.
- Take a benchmark methodology from research experiments to a reusable engineering evaluation framework.

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, constraint handling, and training or model-building costs.
- Adapt and integrate baseline implementations with existing engineering simulation workflows.
- Automate repeated experiments using reproducible scripts and configuration-driven workflows.
- Measure final design quality under fixed engineering evalua

📌 Machine Learning — PhD Intern (Barcelona)
🏢 Empresa Confidencial
📍 Barcelona