Research Fellow (Physics-Informed Machine Learning) Full-time

Research Fellow In Physics-Informed Machine Learning For Metal Additive Manufacturing

The School of Mechanical and Aerospace Engineering (MAE) is seeking to hire a Research Fellow to support the research in Physics-Informed Machine Learning (PIML) for metal additive manufacturing process. This role will focus on developing novel machine learning frameworks that seamlessly integrate physical principles with data-driven modeling for thermal and mechanical simulations in metal additive manufacturing. Candidates will design, implement, and validate PIML models that can accurately capture process physics such as heat transfer, melt-pool dynamics, residual stress evolution, and microstructure formation. Experiments and numerical simulations will be conducted to evaluate the predictive capability, robustness, and generalizability of the proposed models. The research aims to advance intelligent and reliable prediction, diagnosis, monitoring, and performance assessment of metal additive manufacturing processes.

The Research Fellow will work on a cross-disciplinary project at the intersection of mechanics, machine learning, and advanced manufacturing. The key responsibilities of this position include:

• Perform high-fidelity thermal and mechanical numerical simulations for metal additive manufacturing.
• Develop and implement PIML models for analysis and optimization of metal additive manufacturing.
• Integrate physical laws, experimental data, and simulation results into unified machine learning frameworks to improve model robustness and generalizability.
• Conduct data preprocessing, model training, and validation for machine learning tasks.
• Collaborate with internal and external stakeholders on metal additive manufacturing characterization, model validation, and system development.
• Maintenance of lab or equipment or supplies that include procurement and liaison with suppliers.
• Assist or produce high quality reports and documents that consolidate research findings.
• Mentoring PhD and thesis-based master students.
• Assist in grant proposal applications.

Requirements:

• PhD in Mechanical Engineering, Machine Learning, Artificial Intelligence, Computational Mechanics, Material Science, Industrial Engineering, or related fields.
• Strong background in Physics-Informed Machine Learning (PIML), scientific machine learning, or data-driven modeling for engineering systems.
• Expertise in numerical simulation of multi-physics systems, particularly thermal-mechanical analysis in metal additive manufacturing.
• Familiarity with finite element/finite volume methods, high-performance computing, and simulation data processing.
• Strong understanding of research methodologies, data analysis, and statistical techniques
• Ability to work both independently and collaboratively within a team
• Effective written and verbal communication skills, with the ability to convey information, collaborate and build relationship
• Ability to produce technical content for publications and deliver informative presentations to diverse audience including students
• Attention to details and a commitment to upholding ethical standards in all research activities
• Passion in the field of research and a desire to contribute to meaningful projects
• A strong publication track record in relevant fields (e.g., scientific ML, additive manufacturing, computational mechanics) will be an advantage

We regret to inform that only shortlisted candidates will be notified.