Intern Deep Learning And NDE Data Analysis

Job Description Summary

We are seeking a highly motivated postdoc to support development of advanced computer vision and deep learning methods for non-destructive evaluation (NDE) datasets, with a focus on X-ray/CT imaging and experimentation-driven model development. You will work closely with engineers and researchers to build, train, and evaluate models that enhance image quality, extract features, and improve inspection insights for industrial applications.

Job Description

Site Overview

Established in 2000, the John F. Welch Technology Center (JFWTC) in Bengaluru is our multidisciplinary research and engineering center. Engineers and scientists at JFWTC have contributed to hundreds of aviation patents, pioneering breakthroughs in engine technologies, advanced materials, and additive manufacturing.

Key Responsibilities

• Build, train, and evaluate deep learning models for image enhancement, denoising, reconstruction, and feature extraction on NDE image/volume data
• Develop robust data pipelines for preprocessing, augmentation, and efficient 2D/3D batching with GPU acceleration
• Design and run structured experiments (ablations, hyperparameter sweeps), track metrics, and iterate to improve image quality
• Analyze noise/artifacts and apply techniques to boost signal fidelity and effective resolution with clear visualizations
• Package reproducible training/inference pipelines; optimize for speed, memory, and reliability; contribute clean, documented code
• Collaborate with NDE/imaging SMEs, present progress, insights, and recommendations in regular reviews
  
  

Required Qualifications

• Currently pursuing a Master’s or advanced Bachelor’s in Computer Science, Electrical/Computer Engineering, Applied Physics, Data Science, or related field.
• Solid foundation in deep learning for computer vision: CNNs, encoder–decoder architectures, residual/attention blocks, loss functions, and regularization.
• Hands-on experience with PyTorch or TensorFlow, plus Python data stack (NumPy, SciPy, pandas).
• Practical experience training models on image datasets; familiarity with GPU workflows (e.g., CUDA, mixed precision).
• Demonstrated ability to run controlled experiments, maintain clean experiment logs, and interpret statistical results.
• Strong problem-solving skills, curiosity, and attention to detail; ability to work independently and in a team.
  
  

Ideal Candidate

Someone pursuing PhD and not submitted the thesis.

Preferred Qualifications

• Experience with image reconstruction or enhancement in medical/industrial imaging contexts (e.g., X-ray/CT, MRI, ultrasound).
• Understanding of NDE concepts and imaging physics: projections, artifacts, sampling, SNR, resolution.
• Familiarity with classical image processing (OpenCV, scikit-image) and signal processing.
• Experience with 3D data and volumetric processing, including memory-efficient training and inference strategies.
• Knowledge of experiment design (DoE), statistical analysis, and uncertainty quantification.
• Experience with performance optimization: data loaders, mixed precision, vectorization, and profiling.
  
  

Tools and Technologies

• Python, PyTorch/TensorFlow, NumPy/SciPy, scikit-learn, OpenCV, scikit-image
• Visualization: Matplotlib/Seaborn/Plotly
• Optional: CUDA, PyTorch Lightning, DDP, Docker
  
  

GE Aerospace, we have a relentless dedication to the future of safe and more sustainable flight and believe in our talented people to make it happen. Here, you will have the opportunity to work on really cool things with really smart and collaborative people. Together, we will mobilize a new era of growth in aerospace and defense. Where others stop, we accelerate.

Additional Information

Relocation Assistance Provided: Yes

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