Research Fellow (Physics-Informed Machine Learning)

Nanyang Technological University

Job Location:

Singapore - Singapore

Monthly Salary: Not Disclosed

Posted on: 19 hours ago

Vacancies: 1 Vacancy

Job Summary

The School of Mechanical & Aerospace Engineering (MAE) is a robust dynamic and multi-disciplinary international research community comprising of world-class scientists and bright students. MAE prides itself in its excellent research capabilities in areas including advanced manufacturing aerospace biomedical energy industrial engineering maritime engineering robotics etc. The school is equipped with state-of-the-art research infrastructure housing a comprehensive range of cluster laboratories test bedding facilities research centres/institutes and corporate laboratories. Cutting-edge research in MAE addresses the immediate needs of our industries and supports the nations long-term development the new era of industrial 4.0 and sustainable living MAE is rigorous in developing new competencies to support the growth and competitiveness of our engineering sector in the global landscape. MAE has grown to be leader in Engineering Research ranking amongst the top engineering schools in the world.

For more details please view 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.

Hiring Institution: NTU

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.

Hiring Institution: NTU

Key Skills

Bioinformatics
Genetics
R
Biochemistry
Cell Biology
Research Experience
Statistical Software
Cell Culture
Molecular Biology
Data Analysis Skills
Flow Cytometry
Microscopy

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About Company

Nanyang Technological University

Nanyang Technological University is one of the top universities in Singapore offering undergraduate and postgraduate education in engineering, business, science, humanities, arts, social sciences, education and medicine.

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