Physics Informed Machine Learning Engineer

ZEPHYRA; Italia; Ibrido; EUR 50.000 - 70.000; Tempo pieno

Descrizione del lavoro

An innovative engineering startup is seeking a Physics Informed Machine Learning Engineer to develop cutting-edge models for engineering applications. The ideal candidate holds an MSc or PhD in a relevant field and has experience with PDEs and advanced computational tools. This position allows for hybrid or remote work within CET±2. Join us in shaping the future of engineering while making a positive impact on the environment.

Competenze

• Strong background in PDEs, numerical methods, optimization, and scientific computing.
• Experience with GPU training and version control.
• Proficiency in Python and testing.

Mansioni

• Build, train, and validate physics-informed models for engineering problems.
• Fuse simulation and experimental data.
• Solve forward and inverse design problems.

Conoscenze

PDEs

Numerical methods

Optimization

Scientific computing

Python

Formazione

MSc / PhD in Applied Mathematics, Physics, Mechanical/Aerospace Engineering, Computer Science

Strumenti

PyTorch

TensorFlow

JAX

C++

Descrizione del lavoro

Physics Informed Machine Learning Engineer

Position: Physics-Informed ML Engineer

Location: Pont-Saint-Martin (AO), Milan (IT), or Paris (FR) — Hybrid / Remote within CET±2

Employment Type: Full-time

About Zephyra:

Zephyra is the first startup in the world that bridges the gap between the digital and physical worlds. Our clients generate lightweight, thermally efficient designs using advanced numerical analysis, computational geometry, and multi-physics solvers.

Our mission is to help companies reduce environmental impact and increase economic competitiveness globally.

Mission:

1. Build, train, and validate physics-informed models (PIML, differentiable solvers, surrogate models) for thermo-mechanical and fluid/heat-transfer problems.
2. Fuse simulation and experimental data.
3. Solve forward and inverse design problems.

Required Skills:

• MSc / PhD in Applied Mathematics, Physics, Mechanical/Aerospace Engineering, Computer Science, or related fields.
• Strong background in PDEs, numerical methods, optimization, and scientific computing.
• Experience with PyTorch, TensorFlow, or JAX; GPU training.
• Proficiency in Python, version control, testing, and some C++ for integrations.

Nice to Have:

• Experience with physics-informed or differentiable models (PINNs, DeepONet, etc.).
• Knowledge of 3D meshes, unstructured grids, and geometry representations.
• Experience with HPC, multi-GPU, distributed training, and C++ optimization.
• Background in CAD/CAE/manufacturing.
• MLOps and scalable model architecture experience.
• Interest or experience in Agent-Based Modeling.

If you’re excited about shaping the future of engineering, contact us.