Machine Learning Engineer (f/m/d)

What you'll do

• Develop surrogate and reduced-order models, physics-informed ML, and ML pipelines for simulation and experimental data.
• Integrate models with HPC simulation outputs and sensor data; build anomaly detection and predictive diagnostics.
• Apply active learning, Bayesian optimization, and reinforcement learning to guide design exploration and system optimization.

What they ask for

• Master’s or PhD in Machine Learning, Computational Physics, Applied Mathematics, Data Science, Computer Science, or a closely related field.
• Proven experience building, training, and deploying ML models for complex physical systems; strong command of deep learning, probabilistic models, and uncertainty quantification.
• Expert-level Python and experience with HPC environments, including batch schedulers and MPI/OpenMP parallelization.
• Fluency with PDE-based simulation outputs, time-series sensor data, and high-dimensional parameter spaces; hands-on experience with Gaussian processes, neural network surrogates, reduced-order models, or equivalent metamodeling techniques.

Nice to have

• Experience with PINNs or neural operators, such as DeepONet or FNO, applied to physical systems.
• Background in laser, plasma, or high-energy-density physics; digital twin platforms; MDO; reinforcement learning; Monte Carlo methods; or scientific MLOps tooling.

Tools

Python, PyTorch, TensorFlow/JAX, C++, Fortran, COMSOL, ANSYS, MPI/OpenMP

AI focus

Physics-informed and surrogate ML for accelerating simulations, uncertainty quantification, anomaly detection, predictive diagnostics, and autonomous or semi-autonomous system optimization.