ST0246: Internship - Physics-Informed Machine Learning for PDEs
About The Position
MERL is seeking an intern to work on physics-informed scientific machine learning algorithms for problems governed by partial differential equations (PDEs). The ideal candidate would be a PhD student in engineering, computer science, or related fields with a strong background in scientific machine learning for PDEs. Preferred skills include experience with autoencoders, transformers, or diffusion models. Strong coding abilities in Python and a deep learning framework such as Pytorch are essential. The intern will work closely with MERL researchers to develop novel algorithms, conduct numerical experiments, and prepare results for publication. The duration is expected to be at least 3 months with a flexible start date. The pay range for this internship position will be 6-8K per month.
Requirements
- PhD student in engineering, computer science, or related fields
- strong background in scientific machine learning for PDEs
- Strong coding abilities in Python
- experience with a deep learning framework such as Pytorch
Nice To Haves
- experience with autoencoders
- experience with transformers
- experience with diffusion models
Responsibilities
- develop novel algorithms
- conduct numerical experiments
- prepare results for publication
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What This Job Offers
Career Level
Intern
Education Level
Ph.D. or professional degree
Number of Employees
5,001-10,000 employees
