Summary
The MONSTER Lab at the University of Notre Dame is seeking a highly motivated Postdoctoral Researcher to work on a newly awarded federal research program. This position focuses on the intersection of data-driven machine learning, numerical simulation, and advanced manufacturing to develop predictive models for high-performance materials.
Postdoctoral Position in Data-Driven Machine Learning, Numerical Simulation, and Advanced Manufacturing at University of Notre Dame
Designation
Postdoctoral Position
Research Area
- Data-Driven Machine Learning
- Numerical Simulation
- Advanced Manufacturing
- Predictive models for high-performance materials
- Machine-learning surrogate models and property prediction
- Bayesian optimization and active learning for design-of-experiment
- Physics-informed and differentiable modeling of processes
- In-process sensing and characterization data for process digital twins
Location
Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN 46556, USA
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.
- 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:
- Applicants must be U.S. persons (U.S. citizens, lawful permanent residents, or other protected individuals as defined by federal regulation) due to sponsor requirements.
Job Description
The successful candidate will join Prof. Tengfei Luo’s MONSTER Lab to develop predictive models and simulation tools that guide the design and processing of high-performance materials. The role involves closing the loop between computation and experiment, ensuring model predictions are validated and refined against real manufacturing data. Key areas of focus include machine-learning surrogate models, Bayesian optimization, physics-informed modeling, and the use of in-process sensing data to build process digital twins. The position offers substantial opportunities for interaction with industrial partners and experimental collaborators, allowing for direct application of modeling work to manufactured components.
How to Apply
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 with your application; contact information is sufficient at this stage.
Last Date for Apply
Applications will be reviewed on a rolling basis until the position is filled. Early applications are encouraged.
Apply Link
Email application materials to tluo@nd.edu
