The Materials Virtual Lab at the University of California San Diego, led by Professor Shyue Ping Ong, has an opening for a postdoctoral associate in Machine Learning for Materials Design for Energy Storage.

Successful candidates will have the opportunity to lead exciting projects that integrate advanced first principles methods, data science and experiments (through external collaborations) to develop novel materials for a broad variety of applications. Specifically, we are seeking postdoctoral associates to lead our efforts in two areas:
i. Solid-state batteries. We have two projects – one NSF-funded and another industry-funded – to investigate electrode/solid electrolyte interfaces and the design of such interfaces for long-cycle-life, high energy density storage. The success of these efforts can have a major impact on the application of such batteries to large scale applications such as electric vehicles and grid energy storage. The postdoc will have the opportunity to collaborate with other experimental groups, including those from industry research labs, in the pursuit of these goals. Specifically, we are looking to apply machine learning techniques to discover novel materials/materials systems that would yield a significant leap in performance in this promising energy storage technology.
ii. High-entropy materials. This project is part of a NSF funded center focused on the study of complex-concentrated (also colloquially known as “high-entropy”) materials and the interfaces in these CCMs. These materials are particularly exciting due to the potential for tunable properties through composition and processing tuning. Properties of interest include mechanical strength, diffusion kinetics, etc. The postdoc will also have exposure a team of leading faculty in this field.

The common theme is that they feature an interdisciplinary application of materials science and data science to study materials at scales and accuracies hitherto inaccessible via traditional computational techniques. Recent developments from our group include the development of graph deep learning models for property prediction with state-of-the-art accuracies, and the development of machine learning interatomic potentials to model complex systems at large length and time scales. Candidates are encouraged to visit our group website (http://materialsvirtuallab.org) and YouTube Channel (http://www.youtube.com/c/MaterialsVirtualLab) to get an overview of what we do. Like all members of the Materials Virtual Lab, postdoctoral associates will receive in-depth training in the automation of first principles computations, software development and state of the art data science/machine learning techniques. Recent group alumni have gone on to careers in academia (faculty and postdocs) and industry (research labs, Silicon Valley companies, etc.). Postdoctoral associates will also receive personalized mentoring for future leadership positions, including project management skills, proposal writing and effective scientific communication.

The ideal candidate should demonstrate creativity, passion for scientific inquiry, and an ability to link fundamental science to real-world applications. The ideal candidate will also have:
- An advanced degree in materials science and/or solid-state physics.
- Experience with first principles methods, such as density functional theory (DFT), ab initio molecular dynamics, density functional perturbation theory or GW.
- (Optional) Experience with the application of data science to physics, chemistry or materials science
- (Optional) Programming skills, preferably with experience in sustainable software development for robust widely used code bases.

Interested candidates should submit the following via email to Prof Ong ([email protected]):
- 1-page statement summarizing key research interests, accomplishments and future plans
- CV
- (Optional but helpful) A code sample of software developed by the candidate, either in the form of an archive or a link to a publicly-accessible Github/Gitlab repository.

Shyue Ping Ong
Professor
Department of NanoEngineering
University of California, San Diego
9500 Gilman Dr. Mail Code #0448
La Jolla, CA 92093-0448
Tel: (858) 534-2668
Website: https://materialsvirtuallab.org