Postdoctoral Researcher in Artificial Intelligence

About the role

The university seeks a highly motivated postdoctoral researcher to join an interdisciplinary team advancing AI and machine learning for metal additive manufacturing. The work focuses on Electron Beam Powder Bed Fusion (E‑PBF) of refractory alloys, integrating in‑situ sensing, imaging, and AI‑enabled closed‑loop control.

Key responsibilities

• Develop, train, and optimize transformer‑based and other sequence‑learning models for microstructure prediction using E‑PBF parameters and time‑resolved BSE data.
• Process, clean, and curate high‑resolution image and process datasets, building reproducible pipelines for feature extraction and model‑ready data generation.
• Design and evaluate methods linking scan strategies, process parameters, and resulting microstructures, including uncertainty and generalization analysis.
• Contribute to real‑time AI‑enabled closed‑loop control by integrating predictive models with sensor feedback to adapt E‑PBF scan strategies.
• Maintain comprehensive documentation of datasets, model architectures, software, and experiments, and support a well‑organized code repository.

Required profile

• Ph.D. in Computer Science, Engineering, Materials Science, Data Science, Applied Mathematics, or a closely related field.
• At least one year of research experience applying machine learning, deep learning, computer vision, or scientific data analytics.
• Strong written and verbal communication skills and a record of peer‑reviewed publications.

Required skills

• Proficient programming in Python.
• Experience with machine‑learning libraries such as PyTorch, TensorFlow, scikit‑learn, and Keras.
• Knowledge of data processing pipelines, statistical analysis, and visualization tools (matplotlib, seaborn, Plotly).