2027 PhD Graduate - Artificial Intelligence and Complex Systems

Johns Hopkins Applied Physics Laboratory•Laurel, MD

8d•Onsite

About The Position

We are seeking a creative problem solver with strong technical capabilities and initiative to join our team in taking on challenges in complex systems. The Complex Systems Group, part of the Intelligent Systems Center, conducts research at the intersection of AI and complex systems, with an emphasis on advancing capabilities in machine learning-based surrogate models, scientific automation, AI-assisted reasoning, and AI-guided design. We develop methods to drive discovery and design across scientific and engineering domains towards national security impact. This may include AI research in materials science, microbiology, chemistry, earth systems, and other physical and biological systems. As an AI researcher in our group, your primary responsibility will be contributing to and leading AI for Science projects with national security implications. As a member of our research team, you will contribute to AI for Science projects by creating, adapting, and evaluating modern AI methods including machine learning models, frontier AI systems, agentic workflows, physics- and domain-informed learning, AI-driven surrogate models, and closed-loop discovery architectures for scientific and engineering problems. You will establish collaborations with science and engineering domain experts and seek opportunities to propose your ideas for funding. You will also establish working relationships with our Program Managers to identify opportunities to grow our work in key areas for our sponsors. You will publish your research in impactful venues and engage with the external research community, external collaborators, and sponsors.

Requirements

PhD degree in a relevant field such as AI, Mathematics, Statistics, Computer Science, Data Science, or Engineering.
Published previous research in peer-reviewed journals and/or conferences.
Experience in one or more key technical skills, including modern AI/ML methods, deep learning, foundation models, statistical modeling, agentic workflows, physics- or domain-informed ML, AI-driven surrogate modeling, diffusion modeling, neural operators, graph neural networks, large language models, or symbolic AI.
Proficient in Python programming and experience developing AI/ML methods using a modern deep learning framework (e.g., PyTorch, JAX).
Experience with containerized, cloud, High Performance Computing (HPC), or distributed computing environments.
Able to obtain an Interim Secret security clearance by your start date and can ultimately obtain a Top Secret level clearance.
U.S. citizenship.

Nice To Haves

Background knowledge and demonstrated experience in a science or engineering discipline such as materials science, microbiology, chemistry, earth systems, and other physical and biological systems.
Skilled in project management and/or leading technical teams.
Experience writing technical proposals, particularly for government research sponsors.
Experience applying AI/ML to real-world problems involving remote sensing, molecular, chemical, materials, protein engineering, omics, additive manufacturing, and other scientific data modalities.

Responsibilities

Contributing to and leading AI for Science projects with national security implications.
Creating, adapting, and evaluating modern AI methods including machine learning models, frontier AI systems, agentic workflows, physics- and domain-informed learning, AI-driven surrogate models, and closed-loop discovery architectures for scientific and engineering problems.
Establishing collaborations with science and engineering domain experts.
Seeking opportunities to propose ideas for funding.
Establishing working relationships with Program Managers to identify opportunities to grow our work in key areas for our sponsors.
Publishing research in impactful venues.
Engaging with the external research community, external collaborators, and sponsors.