Summary
The Department of Aerospace and Mechanical Engineering at the University of Notre Dame, under the guidance of Prof. Tengfei Luo (Mönster Lab), is seeking a highly motivated Postdoctoral Scholar. This position is supported by a newly awarded federal research program and is available immediately. The successful candidate will contribute to cutting-edge research at the intersection of data-driven machine learning, numerical simulation, and advanced manufacturing, focusing on developing predictive models and simulation tools for high-performance materials.
Postdoctoral Position: Data-Driven Machine Learning, Numerical Simulation, and Advanced Manufacturing
Designation
Postdoctoral Scholar
Research Area
- Data-Driven Machine Learning
- Numerical Simulation
- Advanced Manufacturing
- Predictive Models for Materials Design and Processing
- Physics-informed and Differentiable Modeling
- Process Digital Twins
Location
Department of Aerospace and Mechanical Engineering, University of Notre Dame
Eligibility/Qualification
Required Qualifications
- Ph.D. in Mechanical or Aerospace Engineering, Materials Science, Chemical Engineering, Physics, Chemistry, Computer Science, or a closely related discipline (completed by the start date).
- Strong programming ability, particularly in Python.
- Demonstrated research experience in at least one of the following: machine learning and data-driven modeling; numerical simulation of physical processes; or materials processing and manufacturing.
- Ability to work independently and as part of an interdisciplinary team, and a record of peer-reviewed publication.
Preferred Qualifications
- Experience with modern machine-learning frameworks (PyTorch, JAX, TensorFlow, scikit-learn) and with neural networks, Gaussian processes, Bayesian optimization, or uncertainty quantification.
- Experience with physics-based simulation — finite element or finite volume methods, heat and mass transport, reacting or multiphysics systems, molecular simulation, or first-principles calculations.
- Experience with physics-informed or differentiable programming approaches that couple simulation with learned models.
- Familiarity with materials characterization and manufacturing process data, in-situ process monitoring, or high-temperature materials.
- Experience building and maintaining research databases, workflows, or web tools.
Eligibility (Sponsor Requirements)
Applicants must be U.S. persons (U.S. citizens, lawful permanent residents, or other protected individuals as defined by federal regulation).
Job Description
The Postdoctoral Scholar will work on a newly funded federal research program focusing on the intersection of data-driven machine learning, numerical simulation, and advanced manufacturing. The core objective is to develop predictive models and simulation tools that guide the design and processing of high-performance materials, closing the loop between computational predictions and experimental validation using real manufacturing data. Specific research interests include:
- Developing machine-learning surrogate models and property prediction.
- Implementing Bayesian optimization and active learning for efficient design-of-experiment.
- Creating physics-informed and differentiable modeling of processes.
- Utilizing in-process sensing and characterization data to build and update process digital twins.
There will be significant opportunities to collaborate with an industrial partner and experimental researchers at Notre Dame, translating modeling work into manufactured components.
How to Apply
Please email the following documents to Prof. Tengfei Luo at tluo@nd.edu with the subject line “Postdoc Application — ML/Simulation/Manufacturing”:
- A cover letter briefly describing your research experience and interests as they relate to this position.
- A curriculum vitae including a publication list.
- The names and contact information (title, affiliation, and email) of three professional references.
Please do not send letters of recommendation at this initial stage; contact information is sufficient, and letters will be requested directly from your references if needed.
Last Date for Apply
Applications will be reviewed on a rolling basis until the position is filled. Candidates are encouraged to apply as early as possible.
Apply Link
Email application to Prof. Tengfei Luo: tluo@nd.edu
