PhD intern developing agentic workflows for bioinformatics in a London biotech team. Collaborating with scientists to integrate AI in bioinformatics and metabolomic chemistry applications.
About the role
- 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.
Benefits
- medical
- dental
- vision
- life insurance
- short-term disability
- long-term disability
- 401(k) match
- flexible spending accounts
- flexible work schedules
- employee assistance program
- Employee Scholar Program
- parental leave
- paid time off
- holidays
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