AI ML with CAE

Creative Synergies; Bengaluru; On-site; INR 900,000 - 1,400,000; Full time

Job summary

Creative Synergies in Bengaluru seeks a proficient ML/AI engineer to develop models for engineering and automotive applications, turning complex problems into data-driven solutions.

You will implement PINNs and physics-guided ML, experiment with CNNs, RNNs/LSTMs, Transformers and GNNs using Python, PyTorch/TensorFlow, Scikit-Learn, NumPy and Pandas. Collaboration with CAE teams is key to reducing computation while improving accuracy.

Qualifications

Responsibilities

• Develop and implement Machine Learning and Deep Learning models for engineering and automotive applications.
• Identify engineering problems solvable with AI/ML, predictive analytics, and data-driven approaches.
• Build predictive and surrogate models to estimate simulation/engineering outputs and reduce computational effort.
• Develop Physics-Informed Machine Learning (PINNs) and physics-guided ML models for engineering applications.
• Develop models using Python, PyTorch/TensorFlow, Scikit-learn, NumPy, Pandas, and other relevant ML frameworks.
• Explore and implement Deep Learning, CNNs, RNNs/LSTMs, Transformers, GNNs, and other suitable architectures based on the problem.
• Build AI-driven optimization and DoE approaches for engineering design problems.
• Experience with PhysicsNeMo (formerly NVIDIA Modulus) or similar physics-informed / physics-ML frameworks for developing AI/ML models for engineering and simulation applications.
• Experience in CAE/automotive simulation is an added advantage, particularly exposure to tools such as LS-DYNA, ANSA, GNS Animator, or similar platforms.

Job description

• 1) Develop and implement Machine Learning and Deep Learning models for engineering and automotive applications.
• 2) Identify engineering problems that can be addressed using AI/ML, predictive analytics, and data-driven approaches.
• 3) Build predictive and surrogate models to estimate simulation/engineering outputs and reduce computational effort.
• 4) Develop Physics-Informed Machine Learning (PINNs) and physics-guided ML models for engineering applications.
• 5) Develop models using Python, PyTorch/TensorFlow, Scikit-learn, NumPy, Pandas, and other relevant ML frameworks.
• 6) Explore and implement Deep Learning, CNNs, RNNs/LSTMs, Transformers, GNNs, and other suitable architectures based on the problem.
• 7) Build AI-driven optimization and Design of Experiments (DoE) approaches for engineering design problems.
• 8) Experience with PhysicsNeMo (formerly NVIDIA Modulus) or similar physics-informed / physics-ML frameworks for developing AI/ML models for engineering and simulation applications.
• 9) Experience in CAE/automotive simulation is an added advantage, particularly exposure to tools such as LS-DYNA, ANSA, GNS Animator, or similar platforms.