AI Simulation Engineer (Finite Element Method)

European Tech Recruit; Helsinki; On-site; EUR 90,000 - 130,000; Full time

Job summary

European Tech Recruit is seeking a Senior AI FEM Simulation Engineer to advance nonlinear finite element analysis and AI-driven engineering tools. You will combine computational mechanics with machine learning to speed up structural simulations and enable automations in workflows.

You will collaborate with simulation engineers and AI specialists, translate research into prototypes, and deploy software that accelerates analysis on HPC resources.

Qualifications

• Master's degree or PhD in a technical discipline as listed.
• Strong knowledge of finite element analysis in engineering.
• Solid foundation in numerical methods and machine learning.
• Understanding of structural mechanics and constitutive modelling.
• Experience with nonlinear structural problems and contacts.
• Proficient in Python and C++, or similar languages.
• Experience with Abaqus/ANSYS/COMSOL or equivalent FEM tools.
• Experience with PyTorch, TensorFlow, or JAX.
• Knowledge of LLM architectures and generative AI methods.
• Familiarity with graph neural networks and graph ML.

Responsibilities

• Develop efficient numerical approaches for nonlinear problems.
• Analyze large-scale structural simulation datasets.
• Build machine-learning models from simulation data.
• Develop surrogate and reduced-order methods to accelerate sims.
• Apply graph neural networks to meshes and physical systems.
• Explore LLMs and AI agents for simulation setup and analysis.
• Improve AI models for accuracy, efficiency, and deployment.
• Translate research ideas into prototypes and tools.
• Collaborate with engineers to improve workflows.

Skills

FEM/Computational mechanics

Python

C++

Machine learning

PyTorch

TensorFlow

Graph neural networks

Education

Master's degree or PhD in Mechanical Engineering

PhD in Computational Engineering or related

Tools

Abaqus

ANSYS

COMSOL

Job description

An advanced engineering research and development team is looking for a Senior AI FEM Simulation Engineer to help shape the next generation of computational engineering tools.

This position sits at the intersection of finite element analysis, structural mechanics, scientific computing, and artificial intelligence. You will collaborate with simulation specialists and AI engineers to develop practical technologies that make complex structural analysis faster, more accurate, and easier to automate.

The role

You will apply deep knowledge of FEM and computational mechanics while exploring modern machine-learning approaches for engineering simulation. Your work will span research, prototyping, algorithm development, and the delivery of usable software solutions.

Key responsibilities will include

• Developing efficient numerical approaches for nonlinear problems involving materials, contact, geometry, and large deformation.
• Processing and analysing large-scale structural simulation datasets.
• Building machine-learning models and engineering relevant features from simulation data.
• Developing surrogate models, reduced-order methods, and AI-assisted techniques to accelerate computationally demanding simulations.
• Applying graph neural networks and related architectures to meshes, physical systems, and structural problems.
• Exploring large language models and AI agents for simulation setup, modelling, analysis, optimisation, and interpretation.
• Improving AI models for accuracy, efficiency, scalability, and engineering deployment.
• Identifying new opportunities to combine physics-based simulation with scientific machine learning.
• Translating research ideas into prototypes, software tools, and practical engineering solutions.
• Working closely with structural simulation engineers to improve workflows and productivity.
• Following advances in computational mechanics, scientific machine learning, AI, and engineering simulation.

Your background

• A Master’s degree or PhD in Mechanical Engineering, Materials Science, Computational Engineering, Computer Science, Applied Mathematics, or a related technical discipline.
• Strong knowledge of finite element analysis and its application to engineering simulation.
• A solid mathematical foundation covering numerical methods, numerical analysis, probability, statistics, and machine learning.
• A good understanding of structural mechanics, material behaviour, and constitutive modelling.
• Experience analysing nonlinear structural problems, including material or geometric nonlinearities, contact mechanics, or large deformation.
• Strong scientific programming skills in Python and C++, or comparable languages.
• Experience with an engineering simulation platform such as Abaqus, ANSYS, COMSOL, or an equivalent FEM package.
• Experience with a modern machine-learning framework such as PyTorch, TensorFlow, JAX, or an equivalent.
• Knowledge of large language model architectures and current generative AI methods.
• Familiarity with graph neural networks and graph-based machine learning.
• Strong analytical, numerical, and problem-solving abilities.
• The ability to convert engineering requirements into computational and algorithmic solutions.

Valuable Experience

• Developing AI-enhanced FEM or engineering simulation solutions.
• Applying graph neural networks to meshes, physical systems, computational mechanics, or scientific simulations.
• Creating surrogate models or using machine learning to accelerate numerical simulations.
• Working with physics-informed machine learning, physics-informed neural networks, or neural operators.
• Developing reduced-order or data-driven simulation methods.
• Applying large language models or AI agents to scientific computing, engineering, simulation, or CAE workflows.
• Processing large engineering or simulation datasets.
• Optimising models for inference performance or high-performance AI computing.
• Using parallel computing, GPU acceleration, or HPC for numerical simulation.
• Managing simulation data with database technologies.
• Integrating AI models with established engineering software and simulation platforms.

This is an opportunity to bridge established computational mechanics with rapidly evolving AI technologies and create tools with a direct impact on complex structural analysis.