Intern – CAE & AI-Driven Simulation Workflows

Trisim Technologies, founded in 2016, is a technology consulting firm based in Pune, India. The company provides high-end consulting services to organizations in various fields of emerging and cutting-edge technology. We have worked with multiple clients in the fields of engineering simulations (CFD/CAE), Data Science, software customization along with in-house R&D efforts in modelling and simulation.

About the Role

We are seeking a research-driven intern to explore how open-source CAE simulation tools and modern AI/ML libraries—including NVIDIA Modulus, NVIDIA Omniverse physics APIs, TensorRT/cuDNN, and Julia’s Dyad.jl—can be combined to create next-generation industrial simulation workflows. The goal is to evaluate current technologies and develop a pilot end-to-end workflow demonstrating the value of hybrid physics + AI approaches for engineering design.

Responsibilities

• Explore AI/ML frameworks relevant to physics:
• NVIDIA Modulus, Omniverse, TensorRT, cuDNN
• Julia Dyad.jl and SciML libraries
• PyTorch, TensorFlow, JAX, PINNs, DeepONets, GNOs
• Identify industrial workflows (meshing automation, simulation pipelines, surrogate models, hybrid

physics-AI workflows).

• Build a prototype CAE+AI workflow.
• Assess open-source CAE tools: OpenFOAM, FEniCS, Code_Aster, CalculiX, SU2, Salome, ParaView, Elmer.
• Document architecture, results, and scalability potential.
• Present findings to technical and business stakeholders.

Qualifications

• Master’s degree (or in progress) in Mechanical/ Aerospace Engineering, Computational Science, Applied Physics/Mathematics, with knowledge of Data Science, or similar.
• Understanding of numerical methods, PDEs, and simulation concepts.
• Proficiency in Python.
• Experience with PyTorch or TensorFlow; exposure to JAX is a plus.
• Ability to learn independently and document findings clearly.

Preferred Skills

• Exposure to NVIDIA Modulus or physics-ML.
• Experience with Julia and scientific ML (Dyad.jl, SciML).
• Familiarity with Linux, Docker, Git.
• Interest in AI-accelerated engineering, HPC, cloud-based simulation.
• Understanding of engineering domains (thermal, structural, fluids)

What You Will Gain

• Hands-on experience with leading open-source CAE and physics-ML technologies.
• Experience designing digital-engineering workflows.
• A working prototype showcasing hybrid simulation + AI.
• Exposure to cutting-edge research and industrial practices.
• Opportunities to publish findings or present to leadership.