About the Program

Role Overview

Learning Outcomes

• Apply modern machine learning to realistic engineering simulation and measurement data.
• Learn how surrogate model accuracy depends on data quality, sampling strategy, representation choices, and simulation budget.
• Develop practical experience with parameter-efficient adaptation methods such as LoRA under data-scarce engineering conditions.
• Build fluency with uncertainty estimation, physical consistency checks, and engineering-relevant error analysis.
• Take a research model from Python experimentation toward robust C++ and LibTorch integration.
• Collaborate across machine learning, RF design, simulation, measurement, and runtime engineering disciplines.

• Generate parametric engineering datasets using simulation and/or measurement sources, with attention to sampling strategy and to numerical noise in the underlying data.
• Design, train, and evaluate neural surrogate models for engineering response prediction.
• Investigate how the choice of output representation affects accuracy, particularly across operating regions or frequency ranges where the response varies sharply.
• Establish rigorous baselines and quantify accuracy as a function of simulation or measurement budget.
• Evaluate LoRA and related parameter-efficient methods for specializing trained models to new problem settings, against full and partial fine-tuning.
• Develop calibrated uncertainty estimates so that predictions can be trusted or deferred appropriately.
• Verify physical consistency and engineering constraints - including passivity, causality, and reciprocity where applicable - and report engineering-relevant metrics including worst-case behavior.
• Analyze the simulation corpus and model behavior: parameter sensitivity, response dimensionality, data quality and solver noise, and where prediction error concentrates across frequency and design space.
• Prototype model training and inference integration in C++ and LibTorch within an engineering ML runtime environment.

• Currently pursuing or recently completed a PhD in Machine Learning, Computer Science, Applied Mathematics, Electrical or Electronic Engineering, or a related technical field.
• Hands-on experience across multiple neural architectures - transformers, neural operators, graph networks, CNNs - and sound judgment about which suits a given problem.
• Strong Python and PyTorch, with experience training and debugging neural networks well beyond tutorial scale.
• Strong C++ and hands-on LibTorch experience to implement and integrate models into a production runtime.
• Solid grasp of transfer learning, fine-tuning, and parameter-efficient methods such as LoRA.
• Strong applied statistics and exploratory data analysis, including sensitivity analysis and dimensionality reduction, and the judgment to diagnose a model from its errors rather than its aggregate score.
• Experimental discipline: rigorous baselines, careful ablations, experiment tracking, reproducible results.
• Willingness to learn the underlying physics rather than treat it as arbitrary numerical data. Mentorship will be provided.

• Background in RF, microwave, high-speed digital, or test and measurement engineering; familiarity with S-parameters, filters, or interconnect.
• Experience with EM simulation tools and their scripting interfaces, such as Keysight ADS, RFPro, Momentum, or comparable tools, or engineering measurement and test automation environments.
• Experience with surrogate modeling, operator learning, physics-informed machine learning, or scientific machine learning generally.
• Experience with uncertainty quantification, conformal prediction, or Gaussian process regression.
• CUDA, cuDNN, or TensorRT experience, or prior experience shipping a research model in C++.

• Version control, modular code structure, and reproducible experiment pipelines.
• CMake and Git workflows for sizeable C++ projects, and comfort profiling and debugging C++ code.
• Ability to design an evaluation that answers a question rather than one that produces a favorable number.
• Clear technical communication to both machine learning researchers and engineering stakeholders.

What This Internship Offers

• Apply modern machine learning to physical engineering data in an industrial setting.
• Learn parameter-efficient adaptation and transfer learning under severe data scarcity.
• Develop working fluency in RF and high-speed concepts, measurement data, and simulation workflows.
• Take a research model the full distance, from Python prototype to robust C++.
• The opportunity to shape how machine learning accelerates engineering design and test at a company that builds both the simulators and the instruments that validate them.
• Mentorship across machine learning, simulation, measurement, and RF design, with access to engineering tools and realistic engineering datasets.
• A portfolio-defining project spanning scientific machine learning, efficient model adaptation, and real-world engineering applications.