Bridging AI with Scientific Simulations - Postdoctoral Researcher

Lawrence Livermore National Laboratory•Livermore, CA

56d•Hybrid

About The Position

We have an opening for a Postdoctoral Researcher to perform research in building the core infrastructure for automating the coupling of scientific simulations with Artificial Intelligence (AI). You will work collaboratively with LLNL researchers to design and implement new methodologies that integrate machine learning models and agentic frameworks with large-scale scientific applications targeting heterogeneous, many-core high-performance computing (HPC) systems. This position is in the Center for Applied Scientific Computing (CASC) within the Computing Directorate.

Requirements

Ph.D. in Computer Science or a related field.
Strong programming skills in C, C++, and Python within Unix/Linux environments.
Expert in workflows, orchestration, and data assimilation demonstrate through publications and/or software contributions.
Proficient user of HPC job schedulers (e.g. flux, SLURM) and/or Kubernetes.
Ability to perform research and development in distributed asynchronous software for many-core high performance computing systems.
Ability to conduct high quality research and to develop implementations to evaluate the results.
Proficient verbal and written communication skills necessary to interact in a clear and concise manner, author technical and scientific reports and papers, and deliver scientific presentations.
Ability to take the initiative and have interpersonal communication skills necessary to work effectively in a dynamic team environment.

Nice To Haves

Experience with state-of-the-art machine learning libraries (e.g., PyTorch, Tensorflow).
Experience with performance analysis tools (e.g. Caliper).
Ability to perform research and development in software targeting heterogeneous hardware systems such as CPUs and GPUs.

Responsibilities

Research, design and implement scalable tools combining static and dynamic techniques to couple scientific application execution with machine learning model training and inference.
Research, design and develop mechanisms to coordinate the execution of multiple asynchronous components to support AI Accelerated applications.
Explore novel schemes to achieve robust scalable data assimilation, collect and analyze scientific data and machine learning model training.
Document research by publishing papers in peer-reviewed media and presenting papers within the DOE community and at academic conferences.
Contribute to group grant proposals, including proposal presentations and preparation of proposals that will provide future research opportunities in the field, and participate in the establishment of future research directions.
Pursue independent (but complementary) research interests and interact with a broad spectrum of scientists internally and externally to define and carry out the research.
Perform other duties as assigned.