Job Summary
We are seeking a Machine Learning Research Scientist with experience in Physics-Informed Neural Networks (PINNs), multimodal modeling, and world model development. The candidate will develop AI models that combine physical principles and multimodal data to simulate complex systems and predict real-world behavior.
Key Responsibilities
Develop and implement Physics-Informed Neural Networks (PINNs) and physics-informed machine learning models.
Build multimodal models integrating data such as images, sensor measurements, time-series, and simulation outputs.
Develop predictive world models to learn system dynamics, simulate environments, and forecast future states.
Combine physics-based simulations with data-driven machine learning approaches.
Validate model accuracy, physical consistency, and performance using real-world or simulated data.
Required Qualifications
Master's or PhD in Computer Science, Machine Learning, Applied Mathematics, Physics, Engineering, or a related field.
Hands-on experience with PINNs or scientific machine learning.
Experience in multimodal modeling and/or world model development.
Strong Python programming skills and experience with PyTorch or TensorFlow.
Knowledge of neural networks, physical systems, numerical methods, or simulation.
Preferred Qualifications
Research or industry experience in automotive, robotics, autonomous systems, or physical AI.
Experience with sensor fusion, differential equations, simulation, or predictive dynamics models.
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Relevant research publications or project experience.
