Senior AI FEM Simulation Engineer

Senior AI FEM Simulation Engineer

Location: Helsinki, Finland

About the Role

We are seeking a Senior AI FEM Simulation Engineer to join an advanced engineering research and development team in Helsinki.

This role combines finite element analysis, structural mechanics, scientific computing, and artificial intelligence to develop next-generation simulation technologies. You will work closely with structural simulation specialists and AI engineers to accelerate complex engineering simulations, improve modelling efficiency, and develop intelligent tools for simulation and engineering analysis.

The position is particularly suited to candidates with strong expertise in FEM and structural mechanics who are interested in applying machine learning, graph neural networks, and large language models to computational engineering problems.

Key Responsibilities

• Develop and perform finite element simulations of complex structural systems.

• Investigate efficient numerical approaches for nonlinear problems involving geometry, contact, materials, and other complex physical behaviours.

• Analyse and process large-scale structural simulation datasets.

• Perform feature engineering and develop machine learning models for engineering simulation applications.

• Design and optimise AI algorithms using domain knowledge and simulation data to improve modelling and computational efficiency.

• Develop surrogate models, reduced-order approaches, or AI-assisted techniques to accelerate computationally intensive simulations.

• Explore the application of graph neural networks and other machine learning architectures to mesh-based and structural simulation problems.

• Investigate the use of large language models and AI agents within engineering simulation workflows.

• Develop intelligent tools to support model creation, simulation setup, analysis, optimisation, and interpretation of results.

• Optimise AI models for accuracy, computational efficiency, scalability, and practical engineering deployment.

• Track developments in AI, scientific machine learning, computational mechanics, and engineering simulation.

• Identify new opportunities for combining artificial intelligence with physics-based simulation.

• Collaborate with structural simulation engineers to improve simulation workflows and overall engineering productivity.

• Translate research concepts into practical prototypes, software tools, and engineering solutions.

Essential Requirements

• Master’s degree or PhD in Mechanical Engineering, Materials Science, Computational Engineering, Computer Science, Applied Mathematics, or a related technical discipline.

• Strong understanding of finite element analysis and its application to engineering simulation.

• Strong mathematical foundation in numerical methods, numerical analysis, probability, statistics, and machine learning.

• Good understanding of structural mechanics and material behaviour.

• Knowledge of constitutive modelling and material models used in structural simulation.

• Experience analysing nonlinear structural problems involving areas such as:

  • Material nonlinearities

  • Contact mechanics

  • Geometric nonlinearities

  • Large deformation

• Strong programming skills in Python and C++ or comparable scientific programming languages.

• Experience using commercial or engineering simulation platforms such as Abaqus, ANSYS, COMSOL, or comparable FEM software.

• Experience with modern machine learning frameworks such as PyTorch, TensorFlow, JAX, or equivalent.

• Understanding of large language model architectures and modern generative AI techniques.

• Familiarity with graph neural networks and graph-based machine learning approaches.

• Strong analytical, numerical, and problem-solving skills.

• Ability to translate engineering requirements into computational and algorithmic solutions.

Preferred Qualifications

• Practical experience developing AI-enhanced FEM or engineering simulation solutions.

• Experience applying graph neural networks to meshes, physical systems, computational mechanics, or scientific simulation.

• Experience using machine learning to develop surrogate models or accelerate numerical simulations.

• Experience with physics-informed machine learning or physics-informed neural networks.

• Knowledge of operator learning approaches such as neural operators or related scientific machine learning techniques.

• Experience with reduced-order modelling, model-order reduction, or data-driven simulation techniques.

• Experience applying large language models to engineering, scientific computing, simulation, or computer-aided engineering workflows.

• Experience developing AI agents or automated workflows for simulation setup and analysis.

• Experience processing and analysing large-scale engineering or simulation datasets.

• Knowledge of model optimisation, inference acceleration, or high-performance AI computing.

• Experience with parallel computing, GPU acceleration, or high-performance computing for numerical simulation.

• Familiarity with database systems and frameworks used to manage large engineering or simulation datasets.

• Experience integrating AI models with existing engineering software or simulation platforms.

Candidate Profile

The successful candidate will combine strong fundamentals in computational mechanics and finite element analysis with practical machine learning and software development capabilities.

You should be comfortable working across physics-based modelling, numerical methods, simulation software, data analysis, and modern AI techniques.

The role is particularly suited to an engineer or researcher interested in bridging traditional engineering simulation with scientific machine learning and developing practical AI technologies that can improve the speed, automation, and accuracy of complex structural analysis.