PhD position: Data-driven and semantic framework for redox flow battery modelling, characterisa[...]

Organisation/Company: Fraunhofer SCAI

Department: Virtual Materials Design

Research Field: Chemistry » Computational chemistry, Computer science » Database management, Engineering » Chemical engineering, Mathematics » Computational mathematics

Researcher Profile: First Stage Researcher (R1)

Positions: PhD Positions

Country: Germany

Application Deadline: 18 Jan 2025 - 23:59 (Europe/Berlin)

Type of Contract: Temporary

Job Status: Full-time

Offer Starting Date: 1 Apr 2025

Is the job funded through the EU Research Framework Programme? Horizon Europe - MSCA

Reference Number: DC3-SCAI

Marie Curie Grant Agreement Number: 101168943

Is the Job related to staff position within a Research Infrastructure? No

Offer Description

Background information

Marie Skłodowska-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 “PREDICTOR” 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.

PREDICTOR aims to establish a rapid, high-throughput method to identify and develop materials for electrochemical energy storage. It will enable the rapid identification, 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-flow batteries. Within the project, three demonstrator battery cells (TRL3-4) will be assembled and tested with the newly developed materials.

Project objectives:

• A modelling and simulation tool for the computational screening of organic chemicals based on their potential performance in energy storage systems.
• Automated chemical synthesis, electrolyte production and characterization methods, so that the chemicals identified in the screening step can be rapidly produced and tested for their suitability in energy storage applications.
• Artificial-intelligence-based self-optimization methods that allow experimental data from material characterization to be fed back into automated experimental methods to enable self-driving laboratory platforms and for modelling and simulation tools, improving their accuracy.
• Data management systems to standardize and store the data generated for further use in model validation and self-optimization processes.

The advertised subproject is fully funded by the Marie Skłodowska-Curie European Training Network “PREDICTOR”. It will be carried out by one doctoral candidate at the Fraunhofer Institute for Algorithms and Scientific Computing SCAI (PhD supervision at RWTH Aachen University) over a period of 36 months.

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 electroactive 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 spectra and voltammetry evaluation.

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 the 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.

The Doctoral Network “PREDICTOR” is financed by the European Union under the framework of the program HORIZON Europe, Marie Skłodowska-Curie Actions. The doctoral candidate will be hired for 36 months under contract by Fraunhofer Gesellschaft e.V., with a monthly gross salary of approx. 3200 € (including mobility allowance, but excluding other allowances that depend on eligibility, e.g. family allowance, special needs allowance).

Where to apply

E-mail: jan.hamaekers@scai.fraunhofer.de

Requirements

Research Field: Computer Science, Chemistry, Mathematics, Physics, Engineering (Mechanical and Electrical) - Education Level: Master Degree or equivalent

Skills/Qualifications:

• In accordance with the European Union’s funding rules for doctoral networks, applicants must NOT yet have a PhD.
• Master Degree or equivalent in Computer Science, Chemistry, Mathematics, Physics, Mechanical or Electrical Engineering, or similar.
• Strong interdisciplinary competence (computer science, chemistry, mathematics, physics).
• Outstanding research and development motivation and independent implementation of world-class research topics with top-class partners.
• Desired background: molecular and materials modelling, molecular simulation methods, physical chemistry, and quantum chemistry.
• Required skills: good skills in programming and mathematics, ability to work independently and scientifically.
• Language: English (proficiency both in speaking and writing).

Additional Information

The recruited researcher will have the opportunity to work as part of an international, interdisciplinary team of 17 doctoral candidates, based at universities and industrial firms throughout Europe. She/he will be supported by two mentors within the PREDICTOR project, and will have multiple opportunities to participate in professional and personal development training. Through her/his work, she/he will gain a unique skill set at the interface between modelling and simulation, high-throughput experimentation/characterization and self-optimization and data management over different length scales from nano to the macroscopic level.

She/he is expected to finish the project with a PhD thesis and to disseminate the results through patents (if applicable), publications in peer-reviewed journals, and presentations at international conferences.

All employees at Fraunhofer SCAI benefit from flexible working hours and the option to work from home. Fraunhofer supports an optimal balance between family and career.

Eligibility criteria:

The applicant must not have resided or carried out her/his main activity (work, studies, etc.) in Germany for more than 12 months in the past 3 years.

Selection process

Please send your CV by e-mail, quoting the reference DC3-SCAI to:

An earlier starting date is possible if preferred.

Work Location(s)

Number of offers available: 1

Company/Institute: Fraunhofer Institute for Algorithms and Scientific Computing SCAI

Country: Germany

City: Sankt Augustin

Postal Code: 53757

Street: Schloss Birlinghoven 1