PhD student AI-Driven Probabilistic Tools for High-Fidelity Shape Generation and CFD Predictions (all genders)
Code - AEAB / 716

We are seeking a highly motivated PhD candidate to join our team in the field of Computational Fluid Dynamics (CFD). This exciting task focuses on the development of advanced generative models and their integration into a probabilistic tool for AI applications in state-of-the-art shape generation and CFD predictions. The research will emphasize the application of high-fidelity CFD in aerodynamics of compressors, aiming to push the boundaries of computational engineering for turbomachine applications.
The successful candidate will work on state-of-the-art methodologies to enhance the accuracy and efficiency of CFD simulations through AI-driven generative models. This role offers the opportunity to contribute to transformative advancements in shape generation and CFD predictions, leveraging probabilistic frameworks to address complex aerodynamic challenges.
 

Your Tasks:

•    Develop and validate advanced generative models for high-fidelity shape generation and CFD applications
•    Develop and implement AI-based probabilistic tools for aerodynamic simulation of compressors
•    Validate models through simulations and benchmark datasets
•    Collaborate with experts in compressor design to ensure practical applicability

Your Profile:

•    Master’s degree in aerospace engineering, computer science, applied mathematics, or a related field
•    Strong background in CFD, AI, and probabilistic modeling
•    Proficiency in programming in PyTorch and Python
•    Excellent analytical and problem-solving skills
•    Ability to work independently and in a collaborative research environment

Contact: Heselich, Verena

Phone: +49 89 1489-9451