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The number of serious brain disorders and deaths worldwide caused by diseases of the nervous system has risen sharply in recent decades. Despite huge advances in neuroscience over the past century, our understanding of the brain is still far from complete. To understand the causes and to aid the growing number of affected people, we need to be able to study the brain more closely. New tailored sensors measuring small electromagnetic fluctuations produced by active neurons could contribute to rapidly developing treatments for brain disorders.
We are looking for a project assistant to help the FAIRmat team with:
- Administrative support
- Administration of third-party funds
- Organization of internal and international events
This is an E8 TV-L position, initially limited until September 2026 but with an extension being sought, with the possibility to work part-time. The full job description and instructions on how to apply are also available on our jobs page (EN) or the Humboldt University site (DE).
The second International FAIR-DI Conference on a FAIR Data Infrastructure for Materials Genomics will take online place from July 12-15, 2022. The full program of 78 talks by experts in Data management, Experimental and computational databases, Exascale computing, High-throughput experiments and computations and Machine learning, is now available to view or download here.
Registration for the conference is free and open until June 30, 2022.
Recordings of the FAIRmat tutorial on NOMAD Oasis and FAIR data collaboration and sharing are now available on YouTube !
The YouTube playlist includes the following sessions:
- Talk by Claudia Draxl: From Research Islands and Data Silos to a Powerful Data Infrastructure
- Talk by Markus Scheidgen: Adapting NOMAD Oasis to Your Research
- Hands-on session with Markus Scheidgen: Installing NOMAD, adding parsers, customizing data schemas, creating ELNs
All information about the tutorial including slides & the link to the YouTube playlist can be found on the tutorial event page.
The video of the talk The Next Decade of the US Materials Genome Initiative given by James A Warren at the NFDI Physical Sciences Joint Colloquium on May 5, 2022 is now available on YouTube.
Abstract: The US Materials Genome Initiative has just begun its second decade. With a goal of accelerating the discovery, design, development, and deployment of new materials into manufactured products, the MGI is focused on the creation of a materials innovation infrastructure.
My institution, the National Institute of Standards and Technology (NIST), has framed its support for the MGI around the need for a data infrastructure that enables the rapid discovery of existing data and models, the tools to assess and improve the quality of those data, and finally the development of new methods and metrologies based on that data. In partnership with agencies across the government, academia, industry, and synergistic efforts around the globe, these approaches are now yielding significant advances.
Of particular note is the potential for machine learning and artificial intelligence applications upon these troves of data, which is now being borne out, and the vast consequent opportunities for new discoveries. Additionally, and in light of the many changes in how materials R&D is done, the MGI is has just released a new strategic plan, charting a plan for the next 10 years of an evolving materials innovation infrastructure, which I will review in this lecture.
The link can also be found in our video library or along with other information about the colloquium on the event page.
The first talk of the NFDI Physical Sciences Joint Colloquium will be given by James A. Warren, Director of the NIST Materials Genome Program, on May 5, 2022.
This will be a hybrid event, taking place in Berlin-Adlershof and on Zoom.
More information and registration can be found here.
At our next FAIR-DI - FAIRmat Colloquium on April 7, Hans-Joachim Bungartz will talk about Providing Infrastructure for the Infrastructure.
The Atomic Simulation Recipes (ASR) is an open source Python framework for working with atomistic materials simulations in an efficient and sustainable way that is ideally suited for high-throughput computations. ASR contains a library of recipes, or high-level functions, that define specific atomistic simulations tasks using the Atomic Simulation Environment (ASE). The recipes can be combined into workflows that perform complex simulation tasks while keeping track of relevant metadata to ensure documentation and reproducibility of the data. The ASR also contains functionality for collecting the resulting data into databases and presenting them in a browser.
