PhD – Scientific Machine Learning for Particle-based Fluid Simulation (m/w/div)

The Chair for Mechatronics at the University of Augsburg is seeking a highly motivated and qualified candidate for a PhD position in the field of Scientific Machine Learning. The successful candidate will work on a research project aimed at developing deep learning methods for NeuralODEs for the simulation of particle-based fluid simulation.

What we offer:

• The opportunity to work on cutting-edge research in the field of Scientific Machine Learning
• The possibility to participate in future-oriented practical projects
• A vibrant, dynamic, and international research group
• Flexible working conditions

Qualification:

• A university degree (computer science, mathematics, engineering, or related disciplines) with a strong focus on machine learning
• Knowledge of physical systems simulation with a focus on fluid simulation
• Enthusiasm for the combination of machine learning, physical modeling and simulation 
• Creativity, willingness to perform, teamwork and communication skills

Your tasks:

• You develop learning methods for NeuralODEs for the simulation of physical systems
• You combine modeling approaches from the field of machine learning with laws of physical modeling and simulation to create hybrid models
• You push the boundaries of the level of detail of simulation models

The position is full-time and initially limited to 3 years. The starting date is expected to be June 2023.

The University of Augsburg promotes the professional equality of women. Women are expressly encouraged to apply. The University of Augsburg is particularly committed to the compatibility of family and working life. For further information, please contact the Women's Representative of the Faculty. Severely disabled applicants will be given preference in the case of otherwise essentially equal suitability, ability, and professional performance.

Please send your application (mail or print) to:

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