PhD Intern - Machine Learning for Computational Chemistry

The Physical and Computational Sciences Directorate (PCSD) researchers lead major R&D efforts in experimental and theoretical interfacial chemistry, chemical analysis, high energy physics, interfacial catalysis, multifunctional materials, and integrated high-performance and data-intensive computing.

PCSD is PNNL’s primary steward for research supported by the Department of Energy’s Offices of Basic Energy Sciences, Advanced Scientific Computing Research, and Nuclear Physics, all within the Department of Energy's Office of Science.

Additionally, Directorate staff perform research and development for private industry and other government agencies, such as the Department of Defense and NASA. The Directorate's researchers are members of interdisciplinary teams tackling challenges of national importance that cut across all missions of the Department of Energy.

Responsibilities
 

The Future Computing Technologies Group at Pacific Northwest National Laboratory (PNNL) seeks a Ph.D. intern for summer 2025 with a strong background in machine learning and high-performance computing (HPC).  The internship will be fully remote with a duration of about three months. The candidate will be expected to perform high-impact research focusing on utilizing machine learning for resource estimation for massively parallel chemistry computations. In particular, the candidate will develop regression models to accurately predict the computational resources (costs), such as execution time and power consumption, to guide application users for the selection of optimal application runtime parameters. The expected outcome is high-quality research work, contributing to a potential publication targeting top-tier peer-reviewed conference or journal venues.

Responsibilities and Accountabilities:

• Devising and evaluating regression models and learning strategies based on active and generative learning
• Running simulations for massively parallel chemistry computations on Leadership Class Supercomputing facilities
• Publish results in a top-tier machine learning and high-performance computing conference or journal
• Participate in and lead technical presentations on the work
• Participate in team meetings

Qualifications

Minimum Qualifications:

• Candidates must be currently enrolled/matriculated in a PhD program at an accredited college.
• Minimum GPA of 3.0 is required.

Preferred Qualifications:

• Strong machine learning background with research experience
• Familiarity with coupled-cluster chemistry methods
• Prior research focusing on active and generative learning
• Experience with hyper-parameter optimization
• Familiarity with probabilistic machine learning
• Extensive experience with Python libraries such as Scikit-Learn, SciPy, and Scikit-Optimize
• High-performance computing background and experience