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Job Description

The successful candidate will work with Associate Professor Duane Loh on conducting research at the interface of Machine Learning and Microscopy under a project on Learning Spatiotemporal Motifs In Complex Materials.

The main responsibilities of the position include:

- Conduct cutting-edge research at the intersection of AI and scientific discovery, focusing on identifying spatiotemporal motifs in complex materials.

- Mentor and guide Ph.D. and Master's level students in their research projects.

- Contribute to curriculum design, teaching, and student engagement in AI-for-Science courses and seminars.

- Support the development and deployment of AI-driven tools for High-Performance Computing (HPC) applications.

Qualifications/Requirements

Qualifications / Discipline:

- PhD's degree in Physics, Materials Science, Computer Science, Data Science, Artificial Intelligence, or a related discipline from a strong institution.

Skills:

- Proficient in programming languages such as Python, with experience in using AI/ML libraries (e.g., TensorFlow, PyTorch, scikit-learn).

- Familiar with high-performance computing (HPC) environments and concepts.

- Knowledge of software development practices and version control systems (e.g., Git).

- Exposure to or interest in the application of AI tools to scientific or computational problems.

Experience:

- Completed AI-focused final-year project(s) involving model development, training, and deployment, demonstrating a strong grasp of machine learning principles and practical implementation.

- Participated in or helped organize university events, tech talks, or seminars, indicating familiarity with event logistics and academic engagement.

- Demonstrated ability to work with high-performance computing environments during academic training, with basic knowledge of cluster usage or cloud-based AI tools