Postdoc / Research Associate - Machine Learning and Computational Fluid Dynamics

Your Job:

Our team is dedicated to creating a generic numerical solution framework for SOCs, utilizing an open-source approach, and implementing the innovative concept of a “digital twin.” This framework aims to provide a comprehensive understanding of the transport phenomena occurring within SOCs, incorporating multiscale and multiphysics principles.

• Bridging the Gap: Our primary goal is to bridge the gap between real-world SOC stacks and their virtual counterparts. By combining physical models with data-driven/ML methods, we aim to enhance our understanding of SOC behavior.
  
• Efficiency and Accuracy: Unlike previous efforts that focused mostly on accurate but slow CFD simulations for SOC modeling, our new framework emphasizes achieving a balance between efficiency and accuracy. We believe this approach will significantly expedite the design and optimization processes for SOC stacks and systems.
  
• Neural Network Integration: To achieve this balance, we plan to integrate various neural network methods into our existing framework. These methods will enhance computational efficiency while maintaining accuracy.
  

Your specific tasks in this role include:

• Developing and coding an ML and CFD-based platform based on previously developed framework, openFuelCell2, in collaboration with internal and external partners
  
• Collaborating closely with numerical and experimental colleagues to identify platform requirements and potentially supporting experimental setups
  
• Conducting numerical predictions to assist in the design and optimization of SOC stacks/systems
  
• Supervising (PhD) students involved in related topics
  
• Summarizing and publishing results in scientific publications and conferences

Your Profile:

• Completed Master‘s degree in Mechanical Engineering, Chemical Engineering, Applied Mathematics, or Computational Science, preferably followed by a PhD or equivalent work experience on the relevant field
  
• Proficient programming skills in C++, particularly in ML or artificial neural networks; knowledge of Python is a plus
  
• Knowledge of computational fluid dynamics and its application in electrochemical devices; familiarity with OpenFOAM is advantageous
  
• First experience in interdisciplinary projects with an open-minded and flexible work ethic
  
• A motivated and tolerant attitude, with the ability to responsibly supervise students
  
• Fluent English skills in both speech and writing; knowledge of German is a bonus

Our Offer:

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:

• A large research campus with green spaces, offering the best possible means for networking with colleagues and pursuing sports alongside work
  
• Comprehensive training courses and individual opportunities for personal and professional further development
  
• Extensive company health management
  
• Ideal conditions for balancing work and private life, as well as a family-friendly corporate policy
  
• 30 days of annual leave and provision for days off between public holidays and weekends (e.g. between Christmas and New Year)
  
• Flexible working hours and a full-time position with the option of slightly reduced working hours
  
• Flexible work (location) arrangements, e.g. remote work
  
• Exploration and preparation of next career opportunities supported by our Career Center & Postdoc Office ( https://www.fz-juelich.de/en/career-center-postdoc-office )
  
• Targeted services for international employees, e.g. through our International Advisory Service
  

In addition to exciting tasks and a collaborative working atmosphere in Jülich, we have a lot more to offer: https://go.fzj.de/benefits

The position is for a fixed term of 2 years. Salary and social benefits will conform to the provisions of the Collective Agreement for the Public Service (TVöD-Bund) depending on the applicant’s qualifications and the precise nature of the tasks assigned to them.

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.