Our 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 selfdriving 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 advertized subproject is fully funded by the Marie Skłodowska-Curie European Training Network “PREDICTOR“. It will be carried out by one doctoral candidate at the Institute of Mechanical Process Engineering, PhD supervision under the guidance of Prof. Dr. Hermann Nirschl over a period of 36 months.

The Karlsruhe Institute of Technology (KIT) is “The Research University in the Helmholtz Association.” As the only German university of excellence with a national large-scale research sector, we offer our students, researchers, and employees unique learning, teaching, and working conditions.

In previous projects a framework for the simulationbased screening of electroactive materials for aqueous and nonaqueous organic redox flow batteries (RFBs) has been developed. It adopted a multiscale modelling paradigm, in which simulation methods at different physical scales were further advanced and linked by combining physics- and data-based modelling. The next step is the implementation of control concepts for single- and multi-stage processes (process chain). This will involve the automatic adjustment of power behaviour, material properties, compensation of unforeseen disturbances, acceleration of start-up and shut-down, and planning and control of optimal trajectories under consideration of uncertainties and process relevant quality criteria (e.g. energy and raw material efficiency). Nonlinear, optimization based and robust control methods will be applied. This is a new challenge for RFB and the topic for an innovative Ph.D. thesis.

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 scalesfrom 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.

Qualifications/experience:

• In accordance with the European Union’s funding rules for doctoral networks, applicants must NOT yet have a PhD
• Very good Master in chemical or mechanical engineering or any other background in simulation technologies
• Solid background in the fundamentals of heat and mass transport and in chemical engineering
• Experience in CFD and/or control simulations and programming
• Curiosity and creativity
• Ability to organize and structure an exploratory project
• Fluency in English (min. C1) and if possible Germa