FAIR-DI e.V.
FAIRmat
NOMAD Laboratory
NOMAD CoE

Pillar 2 - Exascale Workflows

Bringing workflows to exscale

Jul 31, 2020

Several Open Positions for master and PhD students as well as Postdocs at several high-level institutions in Europe


Several Open Positions for master and PhD students as well as Postdocs at several high-level institutions in Europe

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.

About NOMAD

The NOMAD Laboratory [1] 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 [2].

  • NOMAD’s FAIR Data Infrastructure empowers the proper sharing of data which furthers research. It is the enabler of a new level, a new quality of science.
  • Findable AI Readiness of data (the second interpretation of the acronym FAIR) enables the detection of structure in data, building “maps of materials properties”, and identification of “genes” that affect or actuate materials properties.

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:

  • A cover letter describing your motivation for applying (max. 1 page) and expressing interest for at least one work package. Please see Overview for a description of nine scientific work packages.
  • A CV with complete description of your academic career and relevant skills (including a description of your programming experience)
  • Contact details of at least two potential referees
  • Copies of your degree certificate(s)
  • For applicants for a PhD position: transcripts of courses taken and grades obtained

Required experience:

  • Excellence in programming in Fortran and/or C++
  • In depth knowledge of Python

Ideally understanding of and experience in some of the following:

  • Computational materials science
  • Machine learning concepts
  • databases, REST APIs, JavaScript, data visualization, GIT, scripts, linux, docker, k8sm

 

Please send your application to Annika Scior.

We are looking forward to hearing from you!

 
[1] 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
[2] 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