{"id":153541,"date":"2024-10-09T03:00:35","date_gmt":"2024-10-09T01:00:35","guid":{"rendered":"https:\/\/www.cde.ual.es\/?p=153541"},"modified":"2024-10-09T08:45:42","modified_gmt":"2024-10-09T06:45:42","slug":"hd-position-data-driven-and-semantic-framework-for-redox-flow-battery-modelling-characterisation-and-design-optimisation-germany","status":"publish","type":"post","link":"https:\/\/www.cde.ual.es\/en\/hd-position-data-driven-and-semantic-framework-for-redox-flow-battery-modelling-characterisation-and-design-optimisation-germany\/","title":{"rendered":"PhD position: Data-driven and semantic framework for redox flow battery modelling, characterisation and design optimisation, Germany"},"content":{"rendered":"
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Marie Sk\u0142odowska-Curie Doctoral Networks are joint research and training projects funded by the European Union. Funding is provided for doctoral candidates from both inside and outside Europe to carry out individual project work in a European country other than their own. The training network \u201cPREDICTOR\u201d is made up of 22 partners, coordinated by Fraunhofer ICT in Germany. The network will recruit a total of 17 doctoral candidates for project work lasting for 36 months.<\/p>\n

PREDICTOR aims to establish a rapid, high-throughput method to identify and develop materials for electrochemical energy storage. It will enable the rapid identi\ufb01cation, synthesis and characterization of materials within a coherent development chain, replacing conventional trial-and-error developments. To validate the PREDICTOR system, the case study will be active materials and electrolytes for redox-\ufb02ow batteries. Within the project, three demonstrator battery cells (TRL3-4) will be assembled and tested with the newly developed materials.<\/p>\n

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Duties of the position<\/strong><\/p>\n

The advertized subproject is fully funded by the Marie Sk\u0142odowska-Curie\u00a0European\u00a0Training\u00a0Network\u00a0\u201cPREDICTOR\u201c.\u00a0It\u00a0will\u00a0be\u00a0carried\u00a0out\u00a0by\u00a0one\u00a0doctoral\u00a0candidate\u00a0at the Fraunhofer Institute for Algorithms and Scientific Computing SCAI (PhD\u00a0supervision\u00a0at\u00a0the RWTH Aachen University)\u00a0over\u00a0a\u00a0period of 36 months.<\/p>\n

Fraunhofer SCAI’s Virtual Materials Design Department has been working on various aspects of multiscale modelling, data analysis, design optimization and machine learning for materials, molecules and especially for electro active materials for flow batteries. This involves in particular also active-learning and generative approaches as well as semantic concepts like for data management. Within PREDICTOR, the objectives of the subproject are the development of semantic concepts (ontology) with a focus on integration of modelling and characterisation data, the development and implementation of analysis and semantic search tools, the optimisation of a high-throughput screening method and the development of AI-based pattern recognition and for spectra and voltammetry evaluation.<\/p>\n

At RWTH Aachen, the chair MODES “Multiscale Modelling of Heterogeneous Catalysis in Energy Systems” is funded within the Cluster of Excellence “Fuel Science Center” (FSC). The research in MODES is driven by computational material design, which is one of the cornerstones in modern development of materials and interface structure for heterogeneous catalysis. MODES uses simulation methods that are built upon path-breaking developments in molecular electronic structure theory and high-performance computing for driving physics-based rational design and optimization of these technologies, which is much more efficient than trial and error-based approaches.<\/p>\n

Required selection criteria<\/strong><\/p>\n