2027 BS/MS - Artificial Intelligence and Complex Systems

Johns Hopkins Applied Physics Laboratory•Laurel, MD

13d•Onsite

About The Position

Are you eager to use artificial intelligence (AI) to unlock insights from complex systems data towards national security impact? Do you want to bring innovative AI to real-world challenges in domains such as materials science, biology, chemistry, and advanced manufacturing? Are you pursuing studies in AI, machine learning, or a related field and looking for a role where you can bring your expertise, initiative, and innovation to AI for Science in interdisciplinary and high-impact ways? If so, we’d love to meet you! We are seeking creative problem solvers with a variety of capabilities and interests, as well as demonstrated initiative, to join our team. The Complex Systems Group, part of the Intelligent Systems Center (www.jhuapl.edu/isc), 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, AI-guided design, and emerging foundation model capabilities such as large language models, retrieval-augmented generation (RAG), and agentic AI systems. We develop methods to drive discovery and design across scientific and engineering domains toward national security impact. This includes AI “co‑investigator” approaches that integrate LLMs with simulation tools, domain knowledge, and complex data to support hypothesis generation, experiment design, and result interpretation. This may include AI research in materials, protein engineering, chemistry, earth systems, and other physical and biological systems. As a member of our research team, you will contribute to projects in a variety of ways, such as designing, training, and developing machine learning (ML) algorithms, collecting and preparing data, evaluating models, programming, developing tool-using and multi-step agentic workflows, and prototyping AI co‑investigator capabilities for scientific discovery. We are committed to developing exceptional talent and fostering a culture of innovation, and as part of our group you will have the chance to grow both within AI and data science, as well as in the complex systems we study.

Requirements

B.S. or M.S. in a STEM field, such as AI, Computer Science, Data Science, Mathematics, Statistics, Physics, or a related discipline.
Proficient in Python programming.
Experience in at least one relevant technical area, such as machine learning, data science, deep learning, graph-based methods, scientific computing, statistical analysis, or software development.
Experience with AI/ML and data science libraries or frameworks such as PyTorch, Hugging Face, NumPy, pandas, SciPy, scikit-learn, or similar tools.
Experience with LLMs, RAG systems, agent frameworks, tool-using AI systems, or multi-step agentic workflows.
Familiarity with, or strong interest in learning emerging AI paradigms such as LLM-based agents, tool-using AI systems, retrieval-augmented generation, or AI-assisted scientific discovery.
Interest in applying AI to scientific or engineering domains such as earth systems, materials science, biology, chemistry, additive manufacturing, or other complex systems.
Able to acquire an Interim Secret level security clearance by your start date and can ultimately acquire a Top Secret level clearance.
U.S. citizenship.

Nice To Haves

Experience applying AI/ML to real-world data such as materials data, omics data, additive manufacturing related data, satellite imagery, remote sensing data, and/or earth systems data
Experience with research software development practices, including Git, testing, documentation, reproducible workflows, or collaborative code review.
Experience with scientific computing, simulation tools, high-performance computing, cloud computing, workflow automation, or scalable model training and inference.
Familiarity with advanced AI methods such as generative models, physics-informed neural networks, surrogate models, graph neural networks, foundation models, active learning, Bayesian optimization, design of experiments, or uncertainty quantification.
Experience performing AI research, especially in a relevant complex systems domain, including developing “AI co‑investigator” capabilities for literature analysis, hypothesis generation, experiment or simulation design, or automated scientific reasoning.
Experience collaborating with scientists, engineers, or domain experts to translate real-world scientific and engineering challenges into AI/ML approaches.
Experience communicating technical results through presentations, reports, publications, prototypes, or open-source contributions.