Research Engineer I

The Research Engineer will work with Government customers, other ARCTOS personnel, and other Government organizations and contractors in the performance of the following duties:

• 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.

• The well-qualified candidate will have expertise in many of these areas:
  • 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.

• Ph.D. in Mechanical Engineering, Aerospace Engineering, Materials Science, or a closely related field.
• U.S. Citizenship is required.

PHYSICAL/WORKING ENVIRONMENT