Intern, Computational Material

Description

Our team has an intern opening for Computational Material Researcher. Candidates will participate in cutting edge research in artificial intelligence and machine learning applications, applying molecular simulation tools (DFT, MD) to resolve material science problems.

• Developing, implementing, and deploying programs and computational solutions employing Machine Learning/Deep Learning for real world problems (computational chemistry and material science).
• Provide research input on early explorations: determine the scope of the problem, the best use of ML, and the merits of different approaches. Provide guidance on data needs (size, type, variability).
• Implementing, debugging, and maintaining computational tools in common programming languages
• Integrating latest research into applied projects.
• Prepare reports, manuscripts, proposals, and technical manuals for use by other scientists.
• Publish results on peer-reviewed journal articles and high impact conferences.

• Master’s degree, Ph.D. or Ph.D. candidate in Computer Science, Material Science, Chemistry, Physics, and related major is preferred.
• Knowledge and experience in Machine Learning Python toolboxes (Num Py, Pandas, PyTorch, scikit-learn, Sci Py).
• Knowledge and experience in molecular simulations including Density Function Theory (DFT) and Molecular Dynamics (MD). Experience in semiconducting material computation and simulation is preferred.
• Experience with ML pipelines, experiments and system evaluation. Experience with hardware-Software Integration (GPU programming, HPC clusters, performance optimization).
• Programming experience with Machine Learning/Deep Learning frameworks (Deep Learning, Graph Neural Networks, Bayesian Optimization, Large Language Models, Reinforcement Learning, etc.).
• Self-motivated detail-oriented team player.
• Good verbal and written communication skills. Have track record of publishing scientific papers.