The NOMAD Center of Excellence offers a dynamic, trans European working environment and team collaboration involving more than 10 academic institutions and high-performance computing centers across Europe (Consortium). We invite talented and skilled master and PhD students as well as postdocs to join our timely and critical efforts for advancing highest level numerical methods, workflows, data infrastructure, and artificial intelligence tools.
The NOMAD Laboratory  maintains the worldwide biggest data base in computational materials science. NOMAD also includes the data from the Materials Project, AFLOW, OQMD and other international data bases by automatic synchronization. The “raw data” of the NOMAD Repository are transformed into a code independent format (Archive). For details see this video. Repository and Archive together are fully FAIR even spearheading what is described the famous paper that introduced the acronym .
In its second phase, the NOMAD CoE will advances this FAIR data infrastructure which also contains a Materials Encyclopedia and an Artificial Intelligence Toolkit. Further emphasis is now placed on computations that address higher complexity of materials (in space and time evolution) and higher accuracy, well beyond that of standard density-functional theory. Keywords are: Exascale Libraries, GW, Coupled Cluster Theory, and Workflows.
The developed methods will be demonstrated in use cases addressing urgent energy, environmental, and societal challenges. Specifically, we will work on catalytic water splitting (hydrogen production) and the transformation of waste heat into useful electricity (search for efficient thermoelectric materials). Such studies are infeasible with present methodology but require new concepts and methods and exascale computers.
How to apply
Your application must include in one pdf file:
Ideally understanding of and experience in some of the following:
Please send your application to Annika Scior.
We are looking forward to hearing from you!
 NOMAD: The FAIR concept for big data-driven materials science, Claudia Draxl, Matthias Scheffler, MRS Bulletin 43, 676 (2018): https://doi.org/10.1557/mrs.2018.208f
 FAIR = findable, accessible, interoperable, reusable/repurposable; The FAIR Guiding Principles for scientific data management and stewardship, Mark D. Wilkinson et al., Scientific Data 3, 160018 (2016): https://doi.org/10.1038/sdata.2016.18