PhD Candidate in Fluid Mechanics and Machine Learning

Job summary

This position offers a 3-year PhD opportunity focused on integrating physics with machine learning for predicting flow fields and heat transfer from limited data. The role is situated within a cross-disciplinary team at a technical university, emphasizing applied research that aims to enhance model reliability for industrial applications.

Qualifications

• A two-year master's degree (120 ECTS points) or equivalent academic level.
• Background in mechanical engineering with a focus on fluid mechanics and heat transfer.
• Experience in machine learning and/or programming, preferably in Python.
• Familiarity with computational modeling for fluid mechanics and mathematical modeling is advantageous.

Responsibilities

• Develop fast and reliable machine learning models that incorporate physics for flow and heat transfer predictions.
• Conduct CFD simulations and utilize datasets for model training, validation, and benchmarking.
• Design and validate physics-informed neural networks (PINNs) and alternative architectures for industrial applications.
• Disseminate research findings through scientific articles and participation in conferences.
• Supervise B.Eng., B.Sc., and M.Sc. students as part of the role.

Skills

• Strong analytical and problem-solving skills.
• Ability to work collaboratively in a diverse team environment.
• Excellent communication skills for presenting research findings.

Education

• A master's degree in mechanical engineering or a related field is required.

Tools

• Proficiency in programming languages such as Python.
• Experience with computational fluid dynamics (CFD) software and machine learning frameworks.