David Broniatowski
6 months ago
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Post-Doctoral Fellow in Physics-Informed Machine Learning for Semiconductor Process Modeling George Washington University in United States
Degree Level
Postdoc
Field of study
Computer Science
Funding
Post-doctoral fellow position associated with the George Washington University and NIST under the CHIPS Act mission. The post does not specify stipend, salary, tuition, or other financial details.
Deadline
Expired
Country
United States
University
George Washington University
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About this position
George Washington University and NIST are seeking a Post-Doctoral Fellow in Physics-Informed Machine Learning (PIML) for semiconductor process modeling in Gaithersburg, Maryland.
The project sits at the intersection of physics, chemistry, computer science, materials science, and mathematics, with a strong focus on atomic layer deposition (ALD), multiscale modeling, and high-fidelity digital twins for chip manufacturing. The role is tied to the CHIPS Act mission to accelerate semiconductor innovation.
Responsibilities include designing and training PIML models constrained by physical laws, developing multiscale simulations from the atomic level upward, implementing and evaluating algorithms, quantifying uncertainty, and building robust Python software tools. The fellow is also expected to present at international conferences and publish in high-impact journals.
Eligibility highlights: a Ph.D. in Chemistry, Physics, Math, Computer Science, or Data Science; familiarity with TensorFlow or PyTorch; experience with process modeling tools such as Cantera or CHEMKIN; strong Python programming skills; and the ability to work with heterogeneous data and uncertainty analysis.
This is a postdoctoral opening, not a scholarship. No stipend or salary amount is stated in the post. The application link is provided in the post, and no deadline is mentioned.
Funding details
Post-doctoral fellow position associated with the George Washington University and NIST under the CHIPS Act mission. The post does not specify stipend, salary, tuition, or other financial details.
What's required
Ph.D. in Chemistry, Physics, Math, Computer Science, or Data Science. Applicants should be familiar with TensorFlow or PyTorch, have experience with process modeling packages such as Cantera or CHEMKIN, be strong in Python for computational modeling, and be able to analyze heterogeneous data sources and assess prediction uncertainty.
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Prepare your application
Review drafts and confirm requirements with the university.
Professors
Professor of Engineering Management and Systems Engineering
United States
Research Interests
Artificial Intelligence
Statistics
Physics
Materials Science
Computer Science
Make your next move.
Prepare your application
Review drafts and confirm requirements with the university.