About the role

Research Engineer developing conditional generative adversarial networks for synthetic microstructures in PMC and CMC. Collaborating with AFRL and advancing materials performance through data-driven insights.

Responsibilities

CGAN Development and Refinement
Retrain and refine a conditional generative adversarial network (CGAN) to generate synthetic PMC and CMC microstructures
Incorporate experimental optical microscopy data into the CGAN training process
Ensure the CGAN accurately models realistic morphologies conditioned on volume fraction and fiber arrangement
Data Integration and Preprocessing
Process and analyze experimental optical microscopy data to prepare it for use in CGAN training
Develop algorithms to extract relevant features (e.g., volume fraction, fiber arrangement) from experimental data
Model Validation and Performance Evaluation
Validate the CGAN-generated microstructures against experimental data to ensure realism and accuracy
Develop metrics and benchmarks to evaluate the quality of synthetic microstructures
Collaboration and Reporting
Collaborate with AFRL researchers and other stakeholders to align CGAN outputs with experimental observations
Document methodologies, results, and findings in technical reports and presentations
Simulation and Analysis
Use the refined CGAN-generated microstructures to simulate residual stress distributions and analyze their impact on material performance
Provide insights into the relationship between microstructure morphology and mechanical properties
Tool Development and Optimization
Develop and optimize computational tools for microstructure generation and analysis
Ensure scalability and efficiency of the CGAN framework for broader applications

Ph.D. in Mechanical Engineering, Aerospace Engineering, Materials Science, or a closely related field
Experience with deep neural networks, LSTM RNNs, GANs, or CGANs
Experience with deep learning frameworks such as TensorFlow or PyTorch
Knowledge of finite element analysis (FEA) or other simulation tools is a plus
Understanding of microstructure-property relationships, especially in CMC materials
Proficiency in processing and analyzing experimental microscopy data
Familiarity with image analysis techniques and feature extraction
Experience with computational modeling of material microstructures and residual stress distributions
Excellent organizational and time management abilities
Desire to work both independently and in a team environment as the project requires
Excellent verbal and written communication skills
Proven track record in conducting both applied and fundamental research, evidenced by publications, patents, or successful technology demonstrations.