Postdoctoral Researcher (Artificial Intelligence)

Prairie View A&M University • Prairie View, TX

4d • Onsite

About The Position

We invite applications for a highly motivated Postdoctoral Researcher to join an interdisciplinary research team advancing artificial intelligence and machine learning for metal additive manufacturing. The position will support Advanced Manufacturing research projects focused on Electron Beam Powder Bed Fusion (E-PBF) of refractory alloys, with emphasis on in-situ backscattered electron (BSE) microstructure sensing, process microstructure modeling, and AI enabled closed-loop process control. The research will also incorporate X-ray computed tomography (XCT) image processing and analysis to characterize internal defects and provide complementary validation of machine learning predictions and in-situ monitoring results. The successful candidate will contribute to data engineering, scientific image and sequence analysis, deep learning model development, benchmarking, real-time prediction and control, reproducible research software, and scientific communication. The position also includes opportunities for research leadership, student mentoring, proposal development, manuscript preparation, and collaborative work with engineering and materials-science researchers. Student supervision, method development, and major research-direction decisions will be carried out in close coordination with the PI and the project team. This position is funded by restricted or grant funds. Continued employment is contingent upon the availability and renewal of restricted or grant funding. The salary is determined in accordance with the University’s compensation structure and will be commensurate with the candidates’ education and experience, within the assigned salary range for this position.

Requirements

• Ph.D. in Computer Science, Electrical or Computer Engineering, Mechanical Engineering, Materials Science and Engineering, Data Science, Applied Mathematics, or a closely related discipline.
• At least one year of research experience applying machine learning, deep learning, computer vision, scientific data analytics, or related data-driven methods.
• Strong programming skills in Python and experience with machine learning libraries such as PyTorch, TensorFlow, scikit learn, and Keras.
• Knowledge of machine learning, deep learning, data analytics, and model evaluation.
• Experience with data processing pipelines, statistical analysis, and data visualization tools including matplotlib, seaborn, and Plotly.
• Strong written and verbal communication abilities and the ability to collaborate with multidisciplinary teams.
• A record of contributing to peer reviewed publications.

Responsibilities

• Develop, train, validate, and optimize machine learning and deep learning models for process microstructure prediction using E-PBF process parameters and time-resolved in-situ BSE data, with particular emphasis on transformer-based and other sequence-learning architectures, in coordination with the PI and project objectives.
• Process, clean, curate, align, and analyze high-resolution image and process datasets; develop reproducible pipelines for data quality assessment, feature extraction, labeling, and model-ready dataset generation.
• Develop and evaluate methods that connect scan strategies, process parameters, in-situ observations, and resulting microstructural features, including assessment of model performance, uncertainty, repeatability, and generalization, in consultation with the PI.
• Contribute to real-time AI-enabled closed-loop control by developing prediction and control approaches that can inform or adapt E-PBF scan strategies based on sensor feedback, in alignment with the PI’s research direction and project goals.
• Integrate machine learning components with data-acquisition and control workflows in collaboration with experimental researchers and engineering collaborators.
• Maintain clear documentation of datasets, model architecture, software, computational workflows, experiments, and results, and contribute to a well-organized research codebase and repository.
• Provide leadership within the research group by taking ownership of project components and coordinating research tasks with collaborators.
• Mentor and assist with the supervision of undergraduate and graduate students in machine learning, data analysis, scientific computing, research methods, and technical communication, in coordination with the PI.
• Train new group members on computational tools, reproducible machine learning workflows, and research best practices.
• Assist the principal investigator with manuscripts, research proposals, technical reports, presentations, and other scholarly outputs.
• Performs other duties as assigned.

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: Ph.D. or professional degree