Staff - Postdoctoral Research Fellow - AI for Science

The successful candidate will have the freedom to define and lead a research agenda aligned with MERL's strategic interests in applying AI to challenging problems arising in industrial and scientific domains. This includes advancing foundational methods while demonstrating impact in real-world systems.

Research Areas of Interest

We are particularly interested in candidates working at the intersection of AI and scientific domains, including but not limited to:

AI for Mathematics and Scientific Computing

• AI-driven methods for optimization, numerical analysis, and large-scale scientific computing
• Learning-based approaches to modeling and analyzing complex dynamical systems
• Integration of machine learning with classical algorithms for improved efficiency and robustness
• Hypothesis formation, sequential experiment design, analysis and deductive discovery
• Theorem development and proving, and algorithm discovery

AI for Physical Sciences

• Physics-constrained or physics-informed AI models for understanding and designing material properties (e.g., optical, electrical, thermal)
• Data-driven and hybrid approaches for multi-scale modeling and simulation
• Differentiable simulators
• Discovery of emergent laws and governing equations in complex physical systems
• Foundational models for thermodynamics and electro-magnetics

AI for Earth and Environmental Sciences

• Methods for multi-scale environmental and geophysical systems
• Remote sensing and spatio-temporal modeling using satellite or sensor data
• Modeling, prediction, and decision-making for environmental and climate-related applications

Responsibilities

• Define and pursue an independent research program in AI for Science, aligned with MERL's research directions
• Develop novel machine learning and AI methodologies for scientific discovery, modeling, and design
• Collaborate with MERL researchers across disciplines to connect fundamental advances with industrial applications
• Publish research results in top-tier conferences and journals
• Contribute to the broader research community through open-source software, datasets, or other artifacts where appropriate

Qualifications

• PhD in Computer Science, Applied Mathematics, Physics, Engineering, or a related field (or expected completion before start date)
• Strong background in machine learning, artificial intelligence, and computational science
• Experience in applying AI to scientific or engineering domains, particularly those domains described above
• Demonstrated ability to conduct independent research, as evidenced by a strong publication record in relevant venues
• Excellent communication skills and ability to collaborate in a multidisciplinary research environment