Machine Learning Engineer (m/f/d)

📋 Description

• Design and deploy surrogate and reduced-order models (ROMs) that replace or accelerate
• Develop physics-informed machine learning (PIML) and physics-informed neural networks (PINNs) that
• Build and maintain ML pipelines for training, validation, uncertainty quantification (UQ), and
• Implement active learning and Bayesian optimization workflows to intelligently guide design space
• Integrate trained ML models into the broader Digital Twin framework, interfacing with HPC
• Develop anomaly detection and predictive diagnostics models to monitor system health and identify

🎯 Requirements

• Master's or PhD in Machine Learning, Computational Physics, Applied Mathematics, Data Science
• Proven experience building, training, and deploying ML models for complex physical systems; strong
• Expert-level Python; proficiency in C++ or Fortran a plus; experience with HPC environments (batch
• Fluency working with PDE-based simulation outputs, time-series sensor data, and high-dimensional
• Hands-on experience with Gaussian processes, neural network surrogates, reduced-order models, or
• Experience building robust ML pipelines for scientific data — including preprocessing, feature

🎁 Benefits

• Incentive compensation, equity, and benefits (as applicable)