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ST0247: Internship - Geometry-Aware Surrogate Modeling for Fluid Dynamics
The Computational Sensing team at MERL is seeking a highly motivated Ph.D. student for a research internship in machine learning for fluid dynamics, focusing on surrogate modeling of free-surface flows in engineered geometries. The goal of this project is to develop geometry-aware and physics-informed surrogate models for complex flow systems, combining high-fidelity simulations with modern neural architectures. The ideal candidate will be a Ph.D. student in engineering, applied mathematics, computer science, or related fields with a solid background and publication record in any of the following areas: operator learning, graph neural networks and geometric learning, particle-based methods, or differentiable simulation frameworks. Programming skills in Python and experience with deep learning frameworks such as PyTorch, TensorFlow, or JAX are required. Publication of the results obtained during the internship is expected. The duration is anticipated to be at least 3 months with a flexible start date.
The pay range for this internship position will be $6-8K per month.
- Research Areas: Applied Physics, Artificial Intelligence, Computational Sensing, Dynamical Systems, Machine Learning
- Host: Wael Ali
- Apply Now
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ST0184: Internship - Uncertainty Quantification & Bayesian Inverse Problems
The Computational Sensing team at MERL is seeking a highly motivated PhD student for an internship focused on uncertainty quantification (UQ) in computational modeling of physical systems. The goal of this project is to advance the methodology and practice of UQ, with a focus on generative models, reduced-order stochastic models, and optimal sensor placement for Bayesian inverse problems. The research will draw upon foundational ideas and techniques in applied mathematics and statistics for applications in wave propagation, fluid dynamics, and more generally high-dimensional systems. The ideal candidate will be a PhD student in engineering, applied mathematics, computer science, or related fields with a solid background and publication record in any of the following areas: generative models, stochastic modeling, dimensionality reduction, Bayesian inference, optimal experimental design, and tensor methods. Programming skills in Python or MATLAB are required. Publication of the results obtained during the internship is expected. The duration is anticipated to be at least 3 months with a flexible start date.
The pay range for this internship position will be $6-8K per month.
- Research Areas: Computational Sensing, Dynamical Systems, Applied Physics, Machine Learning, Optimization
- Host: Wael Ali
- Apply Now
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CI0197: Internship - Embodied AI & Humanoid Robotics
Those who are passionate about pushing the boundaries of embodied AI, join our cutting-edge research team as an intern and contribute to the development of generalist AI agents for humanoid robots. This is a unique opportunity to work on impactful projects aimed at publishing in top-tier AI and robotics venues.
What We’re Looking For
We’re seeking highly motivated individuals with:
- Advanced research experience in robotic AI, edge AI, and agentic AI systems.
- Hands-on expertise in Vision-Language-Action (VLA) models and Foundation Models
- Strong proficiency with Python, PyTorch/JAX, deep learning, and robotic agent frameworks
Internship Details
- Duration: ~3 months
- Start Date: Flexible
- Goal: Publish research at leading AI/robotics conferences and journals
If you're excited about shaping the future of humanoid robotics and AI agents, we’d love to hear from you!
The pay range for this internship position will be 6-8K per month.
- Research Areas: Applied Physics, Artificial Intelligence, Computer Vision, Control, Machine Learning, Robotics, Signal Processing, Speech & Audio, Optimization
- Host: Toshi Koike-Akino
- Apply Now