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PhD Position – Mitigating Degradation in High-Temperature Fuel Cells and Electrolysis for Power[...]

Forschungszentrum Jülich GmbH

Jülich

Vor Ort

EUR 45.000 - 55.000

Vollzeit

Heute
Sei unter den ersten Bewerbenden

Zusammenfassung

Ein renommiertes Forschungsinstitut in Jülich bietet eine PhD-Position zur Mitigation von Degradation in Hochtemperaturbrennstoffzellen. Der Bewerber wird für die Entwicklung neuer Modelle und die Anwendung von CFD und Machine Learning zur Optimierung der Betriebsbedingungen verantwortlich sein. Diese Rolle beinhaltet enge Zusammenarbeit mit interdisziplinären Teams sowie das Verfassen von Publikationen und Präsentationen auf Fachkonferenzen.

Qualifikationen

  • Masterabschluss in Maschinenbau, Verfahrenstechnik oder verwandten Bereichen.
  • Erfahrung in Modellierung und Simulation.
  • Kenntnisse in thermodynamischen, fluidmechanischen und Wärme- und Stoffübertragung.

Aufgaben

  • Entwicklung von Modellen für Multiskalen- und Mehrphysik-Simulationen.
  • Validierung der Modelle mit experimentellen Daten.
  • Anwenden von ML-Methoden in Kombination mit CFD-Simulationen.

Kenntnisse

Modellierung und Simulation
CFD-Tools wie COMSOL
Programmierkenntnisse in Python

Ausbildung

Masterabschluss in Maschinenbau oder verwandten Bereichen

Tools

COMSOL
Ansys Fluent
OpenFOAM
Jobbeschreibung
PhD Position – Mitigating Degradation in High-Temperature Fuel Cells and Electrolysis for Power-to-X Applications

Tätigkeitsfeld Wissenschaft und Technik Ort Jülich bei Köln Arbeitszeit Vollzeit oder Teilzeit Anstellungsdauer Befristet Bewerbungsfrist 31.10.2025 Laufbahn / Entgeltgruppe Gehobener Dienst Kennziffer 2025D-121 Kontakt Forschungszentrum Jülich GmbH

The PHOENIX – Launch Space Power-to-X joint project plays a central role in aligning the innovation cycles of P2X technologies with the long-term goals of the energy transition, the European “Green Deal” and the phase-out of lignite. Funded by the German Federal Ministry of Research, Technology and Space, the project focuses on two central aspects: accelerating technology development and designing sustainable P2X value chains.

As a PhD researcher, you will contribute to the new stack designs for high-temperature electrolysis and fuel cells (SOEC and SOFC). By combining numerical modeling with data-driven approaches, you will identify optimized operating conditions and strategies to improve both steady-state and dynamic performance in fuel cell (biogas) and co-electrolysis applications. To achieve this, you will employ computational fluid dynamics (CFD) and machine learning (ML) to investigate degradation mechanisms under various operating conditions and develop strategies to mitigate long-term performance losses. The solutions you propose will be tested on new stack designs and applied to a broad range of Power-to-X applications. Your main tasks:

  • Develop and integrate degradation models for multiscale and multiphysics simulations of solid oxide cells
  • Validate models using experimental data (e.g., IV curves, EIS measurements)
  • Apply ML methods in combination with CFD simulations to determine operating strategies that can enhance the steady-state and dynamic performance of new stack designs
  • Document, analyze, and evaluate simulation results in the context of the latest scientific literature to address key R&D questions
  • Collaborate closely with interdisciplinary teams at the research center as well as national and international partners from academia and industry
  • Present your results at leading conferences, publish in peer-reviewed journals, and actively contribute to knowledge exchange within the project consortium
  • Master’s degree in mechanical engineering, process engineering, chemical engineering, energy technology, or a related field
  • Proven expertise in modeling and simulation; familiarity with CFD tools, such as COMSOL, Ansys Fluent, or OpenFOAM
  • Excellent knowledge of thermodynamics, fluid mechanics, and heat and mass transfer
  • Strong programming skills in Python, C++, or similar languages
  • Familiarity with machine learning methods for optimization is an advantage
  • Background in fuel cells, electrolysis, or electrochemistry is advantageous
  • Independent and responsible working style with openness to new topics
  • Strong team spirit and motivation to work in an interdisciplinary environment
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