Machine Learning — PhD Intern (Modeling and Data Analysis)

Focus on developing neural surrogate models and LoRA parameter-efficient adaptation for engineering simulation and measurement in C++ and LibTorch.

Originally posted 6 November 2026 by the employer.

About the role

This internship focuses on machine learning surrogate models for engineering simulation and measurement, predicting engineering responses from physical data and integrating models into an engineering runtime environment. You will design neural surrogate models and evaluate parameter-efficient adaptation methods like LoRA across RF and high-speed engineering domains.

What you'll do

• Generate parametric engineering datasets using simulation and measurement sources with attention to sampling strategy and numerical noise.
• Design, train, and evaluate neural surrogate models for engineering response prediction across operating regions and frequency ranges.
• Evaluate LoRA and parameter-efficient methods for specializing trained models against full and partial fine-tuning.
• Verify physical consistency and engineering constraints including passivity, causality, and reciprocity.
• Develop calibrated uncertainty estimates and analyze prediction error across frequency and design space.
• Prototype model training and inference integration in C++ and LibTorch within an engineering ML runtime environment.

What you'll need

• 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, including transformers, neural operators, graph networks, and CNNs.
• Strong Python and PyTorch experience training and debugging neural networks 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.
• Experience with CMake and Git workflows for C++ projects, including profiling and debugging C++ code.

Nice to have

• Background in RF, microwave, high-speed digital, or test and measurement engineering, including 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.
• Experience with uncertainty quantification, conformal prediction, or Gaussian process regression.
• CUDA, cuDNN, or TensorRT experience, or prior experience shipping a research model in C++.

Skills: Surrogate Models, PyTorch, C++, LibTorch, Simulation, LoRA

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