Postdoctoral Position in Reservoir Computing, Machine Learning, and Nonlinear Dynamics (Mechanical Engineering) University of New Mexico in United States

The University of New Mexico is seeking a motivated Postdoctoral Researcher to join Professor Francesco Sorrentino’s research group in the Department of Mechanical Engineering. The research focuses on reservoir computing, machine learning, and nonlinear dynamics, with a special emphasis on developing highly personalized digital twins of the autistic brain. The project leverages advanced reservoir computing (RC) methods, which offer efficient training and are well-suited for analog hardware implementation, enabling fast, low-power computation platforms.

Key research areas include the design and validation of RC architectures with temporal heterogeneity, exploration of critical-like regimes for robustness, noise mitigation strategies for neuroimaging data, and analog hardware implementation using devices such as optical chips, memristors, and nano-oscillators. The interdisciplinary nature of the project connects neuroscience, physics, and engineering, providing a unique opportunity for postdoctoral researchers interested in computational neuroscience, signal processing, and machine learning.

Applicants should hold a PhD in Engineering, Physics, Computer Science, Computational Neuroscience, or a related field. Required expertise includes machine learning, nonlinear dynamics, time-series analysis, or signal processing. Familiarity with reservoir computing, noise filtering, or analog computational hardware is highly desirable. Candidates should be proficient in scientific computing languages (Python, MATLAB, Julia, C++), able to work independently and collaboratively, and ideally have a track record of publications in relevant domains.

The position is a 1-year postdoctoral appointment, renewable based on performance and funding. The successful candidate will have access to UNM’s advanced computational and experimental facilities, with support for career development and professional training. To apply, candidates should submit a cover letter, CV (including publications), and contact details for two referees to Professor Sorrentino at [email protected]. Applications will be reviewed on a rolling basis until the position is filled.

This opportunity is ideal for researchers interested in the intersection of reservoir computing, machine learning, nonlinear dynamics, and digital twin neuroscience, and offers a collaborative environment at a leading US research institution.