PhD position: High-throughput cell characterization and system performance prediction

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 Enerox GmbH, with a monthly gross salary of approx. 2783,63€ (for 14 annual salaries, 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 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.

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 Enerox GmbH, with PhD supervision at Karlsruhe Institute of Technologies (KIT) over a period of 36 months.

We are looking for a motivated PhD candidate for an exciting project at Enerox GmbH. The subproject goal is the development of a mathematical stack behavior model derived from vanadium flow batteries, adaptable for alternative novel chemistries. The fine-tuning of the model will require calibration and validation through experimental work. The model predictions should enable stack and system predictions and accelerate the development of novel green energy storage systems for a safer and cleaner energy transition. The project will involve training events in Denmark, France, Finland, Germany, the Netherlands and UK and secondments in Germany and the Netherlands.

Enerox GmbH is a manufacturer of vanadium flow batteries with over two decades of experience in the field of flow batteries. Flow batteries are non-flammable energy storage systems with lower LCOS than lithium battery systems.

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.

• Versatile projects with a high degree of practical relevance, working at a renominated flow battery company.
• Work-life balance through flexible working hours and support services to help reconcile family and career.
• Further training opportunities.
• Being part of an interdisciplinary and international team.
• Chance to experience different cultures and being part of an international network.
• Fresh fruits, vegetables and free coffee

Requirements Qualifications/experience:

• In accordance with the European Union’s funding rules for doctoral networks, applicants must NOT yet have a PhD.
• Applicants should have a master’s degree in a relevant scientific field; The work will involve experimental chemical laboratory work and development of simulation models.
• Further experience in physical chemistry, electrochemistry, computational chemistry, fuel cells, and flow batteries are of additional advantage. High intrinsic motivation and ambition towards scientific excellence are prerequisites.
• Secondments and project meetings in other EU countries require willingness to travel.
• English language skills are required, and German is beneficial.

Mobility:

The applicant must not have resided or carried out her/ his main activity (work, studies etc.) in Austria 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 Predictor DC13-ENX.

Jie Sun jie.sun@cellcube.com

Application deadline: 30th November 2024