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 Masters or advanced Bachelors in Computer Science Electrical/Computer Engineering Applied Physics Data Science or related field.
• Solid foundation in deep learning for computer vision: CNNs encoderdecoder 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.