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ST0348: Internship - AI-Aided Fluid Modeling and Engineering Design
The Computational Sensing team at MERL is seeking a highly motivated Ph.D. student for a research internship in AI-aided fluid modeling and engineering design. The goal is to develop reasoning-driven and self-improving methods that use simulation feedback to discover and refine physical models, computational algorithms, and engineering designs. Potential directions include automated experimentation, iterative program improvement, and recursive self-improvement of modeling and optimization strategies. 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 one or more of the following areas: foundation models and agentic AI, automated algorithm discovery and refinement, scientific machine learning, computational fluid dynamics, or engineering optimization. Programming skills in Python and experience with machine learning frameworks or scientific computing tools 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, Optimization
- Host: Wael Ali
- Apply Now
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ST0346: Internship - Generative Modeling, Inference, and Uncertainty Quantification
The Computational Sensing team at Mitsubishi Electric Research Laboratories (MERL) is seeking a highly motivated PhD student for a research internship focused on generative modeling, probabilistic inference, and optimization. Potential research directions include diffusion and flow-based generative models, efficient and controllable generation, Bayesian inference and uncertainty quantification, and generative methods for optimization. Applications may include materials and molecular discovery, inverse design, imaging, and modeling of complex physical and dynamical systems. Specific research topics will be developed collaboratively based on mutual interests and expertise. We particularly value creativity, strong mathematical and computational skills, independent thinking, and complementary expertise across disciplines. Publication of research results is expected. The internship is anticipated to last at least 3 months, with a flexible start date.
Specific Requirements:
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PhD student in computer science, applied mathematics, statistics, computational chemistry, materials science, engineering, or a related field.
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Strong research background in one or more of the following areas: generative modeling, probabilistic machine learning, stochastic processes, optimization, or computational modeling of physical and molecular systems.
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Proficiency in Python and experience with modern machine learning frameworks or scientific computing tools.
The pay range for this internship position will be 6-8K per month.
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- Research Areas: Computational Sensing, Dynamical Systems, Applied Physics, Machine Learning, Optimization
- Host: Wael Ali
- Apply Now