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Job Description
The role involves developing physics-based and machine learning algorithms to create digital twin models for batteries, aiming to predict and optimize their safety and performance in real-time within a cutting-edge AI-driven battery research environment.
Key Responsibilities
- Design and develop physics-based battery models and multi-physics simulations to accurately represent cell behavior
- Engineer and apply machine learning and deep learning algorithms for predictive modeling, safety assessment, and performance optimization
- Build and integrate digital twin battery systems using real-time data to monitor and predict safety and performance metrics
- Ensure scalability and robustness of predictive models within big-data systems and infrastructure
- Utilize computational tools like COMSOL Multiphysics and finite element analysis for complex modeling and simulation tasks
Requirements
- Ph.D. in Materials Engineering or a closely related computational engineering field.
- Deep foundational knowledge and practical experience with physics-based battery modeling and computational battery modeling.
- Experience in applying Machine Learning (ML) and Deep Learning algorithms for predictive modeling, safety assessment, and performance optimization, specifically using libraries such as TensorFlow and other neural network architectures.
- Proficiency in core programming languages Python and MATLAB.
- Experience with simulation tools such as COMSOL Multiphysics and finite element analysis (FEA).
- Experience with algorithm infrastructure and architecting digital twin systems.
- Ability to design and develop core physics-based battery models and multi-physics simulations that accurately represent cell behavior.
- Ability to engineer and apply ML and Deep Learning algorithms for predictive modeling, safety assessment, and performance optimization.
- Experience developing AI4Science algorithms that merge materials physics and computational science to solve complex battery challenges.
- Experience in developing and building digital twin battery systems that are trained on real-time cell data to monitor and predict safety and performance metrics.
- Ability to integrate algorithms into big-data systems and infrastructure, ensuring models are scalable and robust.
- Maintain a hybrid understanding of data science, materials physics, algorithm infrastructure, and AI models to ensure model validity and utility.
- Experience with computational tools like COMSOL Multiphysics and finite element analysis for complex modeling and simulation tasks.
Benefits & Perks
A highly competitive salary
Robust benefits package including comprehensive health coverage
Attractive equity stock options program
Opportunity to contribute to meaningful scientific projects with broad public impact
Work in a dynamic, collaborative, and innovative environment
Significant opportunities for professional growth and career development
Access to state-of-the-art facilities and proprietary technologies
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