Postdoctoral Researcher in Scientific Machine Learning for Constrained Optimization and Control at Johns Hopkins University Johns Hopkins University in United States

Johns Hopkins University is hiring a Postdoctoral Researcher in Scientific Machine Learning (SciML) within the Department of Civil and Systems Engineering, the Ralph O’Connor Sustainable Energy Institute (ROSEI), and the Data Science and AI Institute (DSAI).

The position is part of the SOLARIS Lab led by Ján Drgoňa, an Associate Professor at JHU. The lab works at the intersection of scientific machine learning, differentiable programming, optimization, control, and energy systems, with applications to large-scale sustainable energy systems.

The research focus of this postdoc is foundational SciML for constrained optimization and control, especially control of partial differential equations (PDEs) and mixed-integer programming (MIP). The post also highlights related interests in learning to optimize (L2O), decision-focused learning, physics-informed machine learning (PIML), and neural operators.

Eligibility highlights: applicants should hold a PhD in Control, Computer Science, Applied Math, Operations Research, Industrial Engineering, or a related field. Strong applied mathematics foundations are required. Experience with Python or Julia, PyTorch or Jax, and open-source software development is preferred. Prior work in L2O, decision-focused learning, PIML, or neural operators is especially relevant.

Funding: the post is advertised as a full-time on-site postdoctoral role; no stipend or salary amount is stated in the post.

How to apply: interested candidates should email their CV to [email protected]. Additional research context is available on the linked personal website, lab website, and GitHub repository pages.

Application window: no deadline is provided in the post.