Tenure-track faculty position at all levels in Data Science and Artificial Intelligence for Mechanical Engineering

Description

The Department of Mechanical Engineering at Johns Hopkins University invites applications for tenure-track or tenured faculty positions to lead the transformation of mechanical engineering through data science (DS) and artificial intelligence (AI). We seek scholars who advance the field through foundational contributions to DS and AI, as well as apply these methods to address grand challenges in mechanical engineering. We welcome candidates whose research pushes the boundaries of what is possible when deep expertise in DS/AI intersects with core mechanical engineering domains, including fluid dynamics, mechanics and materials, dynamics and controls, robotics, biomechanics and bioengineering in human health, energy systems, space engineering, planetary health, and climate resilience.

Examples of DS/AI-centric research thrusts of interest to Mechanical Engineering include, but are not limited to:

•         AI-accelerated modeling and simulation for multiphysics and multiscale problems

•         Physics-informed and hybrid learning for reduced-order and surrogate models

•         Uncertainty quantification, verification and validation, and risk-aware decision-making

•         Learning-enabled estimation, planning, and control for autonomous and human-in-the-loop systems

•         Digital twins and lifecycle analytics for complex engineered systems

•         Data-centric engineering, including sensing, data assimilation, experimental design, and streaming analytics

•         Generative and inverse design with autonomous design exploration

•         Edge and embedded AI for real-time inference and control

•         Foundations of trustworthy, robust, and interpretable AI for engineering decision-making

•         Agentic and Agent-based approaches

•         Computer vision

•         Natural Language Processing

•         DS/AI for resilience, sustainability, and planetary health

Johns Hopkins University has made a landmark investment in data science and artificial intelligence by establishing the Johns Hopkins Data Science and AI Institute (DSAI Institute). The Whiting School of Engineering at Johns Hopkins University is adding ~150 new faculty members over the next few years, constructing a new half-million-square-foot building to house the DSAI Institute, and deploying unparalleled computational infrastructure to support cutting-edge research and collaboration in this domain. This transformative initiative will establish Johns Hopkins University as one of the nation's largest and most distinguished engineering schools, featuring a world-leading AI research program. Faculty in the Department of Mechanical Engineering will play a vital role in this vision, with opportunities to engage across disciplines, access exceptional resources, and shape the future of data science and AI in engineering.  

The Department of Mechanical Engineering at Johns Hopkins leads distinctive research programs. Its research spans core and emerging areas, including Mechanics and Materials, Robotics, Fluid Mechanics and Thermal Processes, Systems Modeling and Control, Mechanical Engineering in Biology and Medicine, Energy and the Environment, Micro/Nanoscale Science and Engineering, and Space Engineering. Its faculty thrives within a vibrant ecosystem of interdisciplinary collaboration that positions its faculty at the forefront of DS and AI innovation. The department operates the Johns Hopkins Turbulence Databases (JHTDB), a multi-terabyte open numerical laboratory that provides programmatic access to DNS datasets for data assimilation, model discovery, and ML benchmarking. Additionally, its faculty members are key contributors to the Artificial Intelligence for Materials Design Laboratory (AIMD-L). This automated high-throughput experimental facility generates rich microstructure-property datasets for AI-driven materials discovery and inverse design in extreme environments. Faculty also engage across an exceptional network of university institutes and centers that amplify DS and AI research impact including: the Laboratory for Computational Sensing and Robotics (LCSR) for learning-enabled perception, planning, and control; the Institute for NanoBioTechnology (INBT) for bioengineering research and discovery, the Hopkins Extreme Materials Institute (HEMI) for data-driven materials discovery and design; the Institute for Assured Autonomy(IAA) for trustworthy and veri