Two postdoctoral openings in Scientific Machine Learning in Johns Hopkins University

Professors Michael Shields, Yannis Kevrekidis, Lori Graham-Brady, Tamer Zaki, Dimitris Giovanis, and Somdatta Goswami are actively seeking candidates for two postdoctoral positions at Johns Hopkins University. These positions offer a unique opportunity to engage in groundbreaking research at the intersection of multiple scientific disciplines, utilizing Scientific Machine Learning to address complex real-world engineering challenges.

Successful candidates will contribute to an innovative collaborative project that aims to advance the frontiers of machine learning in the context of physical systems. The primary focus of this research endeavor is the development of pioneering methods for learning physics and addressing uncertainty within operator frameworks. This research is positioned at the forefront of the field, pushing the boundaries of what is currently achievable in machine learning applied to the study of physical systems.

We strongly encourage recent graduates with expertise in the following areas to apply for these positions:

1. Any branch of SciML including, but not limited to physics informed neural networks and neural operators
2. Dimension reduction, including latent space or manifold learning
3. Bayesian neural networks, Variational Inference, and other inference methods on high-dimensional systems
4. UQ for complex, high-dimensional systems

The postdoc will be expected to work well with a team that includes mentoring PhD students, collaborating with other postdocs, and working with multiple faculty members. The successful candidates must be highly self-motivated, with the ability to work both within the team and independently. Each successful candidate will be assigned a primary and secondary mentor from the list of faculty above based on their expertise, interests, and skill set. A Ph.D. in a related engineering field, physics, applied mathematics, or computer science is required. The positions are open effective immediately. 

Details of the project can be found here.
If you are interested in the position, please email your CV to Prof. Michael Shields (michael.shields@jhu.edu).