PhD and Postdoctoral Positions in Quantum-Enhanced Bayesian Inference, Stochastic Systems, and Aerospace Applications

Fotis Kopsaftopoulos, Associate Professor of Aerospace Engineering at Rensselaer Polytechnic Institute (RPI), is recruiting PhD students (for Fall 2026) and a Postdoctoral Researcher to join his research group. The group focuses on advanced topics such as quantum-enhanced Bayesian inference, Markov Chain Monte Carlo (MCMC), stochastic dynamical systems, time series foundation models, and data-driven modeling. Research applications span aerospace systems, structural and material awareness, structural health monitoring (SHM), additive manufacturing, and aircraft/drone state estimation and diagnostics. Ideal candidates will have a background or strong interest in Bayesian inference, uncertainty quantification, inverse problems, stochastic systems, and time-series machine learning (including representation learning and transformers). Experience with physics-informed modeling (FEM/CFD/ROMs), coupling data-driven and physics-based methods, and quantum algorithms is highly valued. Proficiency in Python or Matlab programming is required. This opportunity is ideal for students and researchers interested in the intersection of aerospace engineering, mathematics, and computer science, particularly those passionate about applying advanced modeling and machine learning techniques to real-world engineering problems. The group offers a collaborative environment at RPI, a leading US research institution. Interested applicants should contact Prof. Kopsaftopoulos via LinkedIn or email to discuss their interest and fit for the group. The application window is for Fall 2026, with a deadline of December 28, 2025. No specific funding details are provided in the announcement.