Postdoctoral Researcher for Process Modeling Using Physically-Informed Machine Learning

National Institute of Standards and Technology; Gaithersburg (MD); Sur place; USD 55 000 - 75 000; Plein temps

Résumé du poste

National Institute of Standards and Technology (NIST) seeks a Postdoctoral Researcher to advance process modeling through physically-informed machine learning. The role develops in situ metrology tools and multiscale simulations for atomic layer deposition, combining data with physics constraints to quantify mass transport and chemical processes.

Applicants should hold a PhD in engineering, physics, or materials science, with experience in Cantera/CHEMKIN and ML frameworks (TensorFlow/PyTorch).

Qualifications

• Ph.D. in engineering, physics, materials science, or related field.
• Strong coursework in chemistry, physics, math, statistics or CS.
• Familiarity with chemical process modeling packages and AI/ML software.
• Proficiency in Python programming.

Responsabilités

• Design and train physics-informed ML models for predicting properties using experimental/computational data.
• Assess performance and uncertainty of PIML models.
• Develop multiscale modeling systems for ALD processes.

Connaissances

Python

TensorFlow

PyTorch

Physics-informed ML

Communication

Formation

Ph.D in engineering/physics/materials science

Outils

Cantera

CHEMKIN

Description du poste

Postdoctoral Researcher for Process Modeling Using Physically-Informed Machine Learning

The Material Measurement Laboratory of the National Institute of Standards and Technology (NIST) is seeking qualified persons (U.S. citizens preferred) to join a multi-disciplinary team of scientists working on advanced modeling of atomic layer deposition (ALD) processes. The project will develop a suite of in situ metrology tools to quantify mass transport processes occurring during thermal ALD. This position requires expertise in the use of machine learning to model chemical processes. The candidate should be knowledgeable in chemical process modeling and in parameter estimation in complex models using regression and machine learning. Direct experience with deep learning would also be advantageous.

Duties

• Designing and training physics-informed machine learning (PIML) models for the prediction of physical and chemical properties using data from experiments and computation constrained by physics requirements.
• Implementing algorithms to assess the performance of PIML models and assess uncertainty in the predictions of PIML models.
• Developing systems and software for multiscale modeling of atomic layer deposition processes.

Required Skills, Expertise, and Qualifications

• A Ph.D degree in engineering, physics, materials science, or a related field.
• Significant course work in chemistry, physics, mathematics, statistics and/or computer science.
• Familiarity with one or more chemical process modeling packages (e.g. Cantera, CHEMKIN) and one or more AI/ML software packages (e.g. Tensorflow or Pytorch).
• Ability to program in a modern computational language (e.g. Python).
• Strong oral and written communication skills.

Employment Terms

• This opportunity is to be an associate researcher in the NIST Material Data Division for a term of 1 year, with options to renew.
• Associate researchers are NOT Federal Employees, but they work along with NIST researchers and with NIST’s world-class instrumentation.
• Relocation expenses will not be provided.
• U.S. citizens hired into associate positions may have the opportunity to seek longer term Federal Employment.

An Associate at NIST is not a federal employee but collaborates with NIST on research projects of mutual interest and does not work exclusively on NIST mission-related activities. If you have any questions, please contact the International and Academic Affairs Office at [e-mail].