Physics AI Intern developing algorithms for Physics-Informed Machine Learning at RTX. Collaborating with leading researchers in aerospace to advance physics AI concepts.
Responsibilities
Develop algorithms, implement software, and train models for novel Physics-Informed Machine Learning (PIML) or Scientific Machine Learning (SciML) architectures.
Incorporate physics into deep learning architectures in a semi-intrusive manner when partial physics models are available.
Demonstrate the use of PIML to learn spatiotemporal fields from sparse data.
Implement and operationalize PIML/SciML systems autonomously using agentic AI.
Design and conduct experiments on GPU clusters, benchmarking performance against established baselines.
Present research findings through presentations, technical reports, and contributions to top-tier publications.
Collaborate with a focused team to develop advanced physics AI tools.
Requirements
Currently pursuing a Ph.D. in Computer Science, Mathematics, or a related Engineering discipline with a focus on physics AI.
Candidates must not graduate before December 2026.
A copy of your academic transcripts must be submitted with your application.
At least 1 year of Ph.D.-level research experience in the broad field of physics AI.
At least 1 year of machine learning software development experience in Python, with proficiency in deep learning frameworks such as PyTorch or TensorFlow.
Familiarity with simulation tools such as ANSYS, COMSOL, or similar platforms.
Must be authorized to work in the U.S. without sponsorship now or in the future.
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