Advancing machine learning capabilities through image-based synthetic data generation for materials science, manufacturing, and metrology applications.
Requirements
- Experience with Python and libraries such as OpenCV, PIL, scikit-image, or PyTorch/TensorFlow.
- Familiarity with generative AI techniques and image synthesis.
- Knowledge of ML model development and evaluation.
- Experience with 3D rendering or simulation tools (e.g., Blender, Unity).
- Prior work with image segmentation, classification, or object detection.
Responsibilities
- Design and implement pipelines for generating synthetic images using simulation tools and generative models (e.g., GANs, diffusion models).
- Apply image processing techniques to enhance, annotate, and transform raw and synthetic datasets.
- Collaborate with ML engineers to integrate synthetic data into model training workflows.
- Evaluate the impact of synthetic data on model performance using metrics such as precision, recall, and robustness.
- Contribute internal documentation and present findings to cross-functional teams.
Other
- Strong communication and collaboration skills.
- Exposure to materials science or manufacturing domains.
- Pursuing a PhD degree in Computer Science, Electrical Engineering, Data Science, or related field and must be enrolled in Fall 2026 classes.
- Travel: None
