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In the department “Electrochemical Process and System Technologies” of IEK-9, research and development activities in the field of solid oxide cells (SOCs) focus primarily on optimizing the performance and lifetime of stacks and systems for various applications. SOCs are electrochemical energy converters with a solid oxide ceramic electrolyte, which are typically operated at high temperatures and can achieve very high efficiencies. When operated as a fuel cell (Solid Oxide Fuel Cell: SOFC), electrolysis (Solid Oxide Electrolysis Cell: SOEC) or co-electrolysis, or even in reversible mode (reversible Solid Oxide Cell: rSOC), the SOC offers great potential for defossilization of a wide range of applications. In order to exploit this potential in the future, it is necessary to improve the competitiveness of SOCs by increasing performance, reducing degradation and minimizing manufacturing costs. The aim of this master thesis is to investigate the applicability of different Machine Learning (ML) methods for degradation analysis, quantification or classification. The focus is on long-term operating data of SOC stacks operated in both fuel cell and electrolysis modes. Your tasks will include the following:
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We work on the very latest issues that impact our society and are offering you the chance to actively help in shaping the change! We support you in your work with:
The position is initially for a fixed term of 6-12 months.
We welcome applications from people with diverse backgrounds, e.g. in terms of age, gender, disability, sexual orientation / identity, and social, ethnic and religious origin. A diverse and inclusive working environment with equal opportunities in which everyone can realize their potential is important to us.
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