PhD position: Automated potentio-dynamic electrochemical characterization

Recruiting organisation

Fraunhofer-Institute for Chemical Technology (ICT)

Subproject title

Automated potentio-dynamic electrochemical characterization

Starting date

1st April 2025 (or earlier if preferred)

Salary

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).

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 candida- tes 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.

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 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.

Job description

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 Fraunhofer Institute for Chemical Technology ICT (PhD supervision at Universität der Bundeswehr München) over a period of 36 months.

Fraunhofer ICT's Applied Electrochemistry Department has been working on various aspects of electrochemical methods, converters and storage systems since 2007. Within PREDICTOR, a module will be developed that enables automated electrochemical measurements such as cyclic voltammetry and impedance spectroscopy on electrolytes. It should be possible to integrate the module into an entire system so that independent measurements and optimization of electrolytes can be carried out by means of machine learning, data management and communication via various interfaces to other modules. Subsequently, investigations will be carried out on electrochemical optimization issues of electrolytes.

Benefits

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

Requirements

Qualifications/experience:

• In accordance with the European Union’s funding rules for doctoral networks, applicants must NOT yet have a PhD
• Degree in chemistry, physics, mechanical engineering, electrical engineering or similar
• Strong interdisciplinary competence (chemistry, physics, basic information technology, electrical and mechanical engineering)
• Outstanding research and development motivation and independent implementation of world-class research topics with top-class partners

Mobility:

• 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.

How to apply
Please send your CV by e-mail (preferred) or by post, quoting the reference DC2-ICT

Carolyn Fisher
Carolyn.fisher@ict.fraunhofer.de

Fraunhofer ICT
Joseph-von-Fraunhofer-Str. 7
76327 Pfinztal
Germany

Application deadline:  8th November 2024