🚨 Hiring a Postdoctoral Researcher in Scientific Machine Learning (SciML) at Johns Hopkins University 🚨 Was your PhD about cutting-edge Scientific Machine Learning (SciML)? If yes, I’d be glad to hear from you. Role: Full-time on-site role for a postdoc at the Department of Civil and Systems Engineering and the Ralph O'Connor Sustainable Energy Institute (ROSEI) at JHU. The role focuses on foundational research in SciML for constrained optimization and control with applications in large-scale energy systems. Qualifications: PhD degree in Control, Computer Science, Applied Math, Operations Research, Industrial Engineering, or a related field Rigorous applied math foundations Prior research experience in learning to optimize (L2O) and decision-focused learning Prior research experience in physics-informed machine learning (PIML) and neural operators Proficiency in at least one programming language (Python, Julia) Proficiency in the use of differentiable programming libraries (PyTorch, Jax) Demonstrated software development skills via open-source contributions If you're passionate about advancing research in SciML for decision-making with a real-world impact, I invite you to apply. 🔬 For more details about my research, visit: solaris-jhu.github.io/solari… github.com/SOLARIS-JHU engineering.jhu.edu/case/fac… drgona.github.io/ 📧 Interested candidates can send their CVs to [email protected].

New paper  in Energy and Buildings: “Physics vs. Structure: A Systematic Benchmark of Learning Strategies for Multi-Zone Building Thermal Dynamics.” We provide an extensive benchmark that systematically compares a range of modeling approaches, from linear models to neural ODEs and physics-constrained models, for data-driven modeling of thermal dynamics in buildings. Besides prediction accuracy, we thoroughly quantify computational cost and physical consistency of the models, which is critical for HVAC control applications. paper: sciencedirect.com/science/ar… All the experiments have been made possible through the Neuromancer scientific machine learning library: github.com/pnnl/neuromancer Thanks to my PNNL colleagues Soumya Vasisht, Cary Faulkner, Elad Michael, Aaron Tuor, and Draguna Vrabie for this fruitful collaboration, as well as the funding from @ENERGY that supported this work.