We invite applications for the following postdoctoral position at the University of Wisconsin – Madison with Prof. Dane Morgan in the Department of Materials Science and Engineering: 

Development of Machine Learning Materials Innovation Infrastructure

This work is focused on the development of computational infrastructure for machine learning applications in materials science and engineering. The infrastructure integrates easy access to data, cloud-based tools for application of machine learning algorithms, and human- and machine accessible dissemination of machine learning tools. It is expected this work will enable transformative discoveries in materials science, including accelerated design of new materials based on machine learning models, and the creation of new modalities of materials structural and functional property characterization across length and time scales. This work will enable career-expanding interaction and collaboration with many researchers in the field and includes of team of materials scientists, computer scientists, and software developers.

A PhD in materials science, computer science, or a related field is required. Familiarity with machine learning and data science tools (e.g. scikit-learn, Keras) and a strong programming background is preferred, but capable and enthusiastic applicants with varied background will be considered.

Additional information

The appointment is initially for one year with the expectation of this being extended if the work is going well.  Interested applicants should send (1) CV in PDF format, (2) a brief cover letter describing suitability for the position, and (3) contact information for three references to Ryan Jacobs at [email protected]. Review of applicants will begin immediately and will continue until the position is filled.

Professor Dane Morgan and the Computational Materials Group (CMG) (http://matmodel.engr.wisc.edu/):  CMG is a joint effort of Izabela Szlufarska and Dane Morgan in the Department of Materials Science and Engineering at UW Madison.  We are a highly active group leveraging simulation, machine learning and experiment and work in areas including nuclear materials, amorphous alloys, nanomechanics, nano-bio interfaces, fuel cells, batteries, semiconductors, and geophysics.