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Machine Learning Scientist
About Us
At SES AI, we are at the forefront of revolutionizing lithium-metal battery creation with our groundbreaking approach that integrates cutting-edge machine learning techniques into our research and development processes. Our mission is to lead the next wave of scientific discovery in material science, powered by advanced AI technologies with a dedication to AI for Science.
To learn more about SES, please visit: www.ses.ai
Position Scope
The SES AI Prometheus team is expanding its AI research team. We have been building a cutting-edge, machine-learning-enabled capability to design and optimize chemicals for discovering novel lithium-metal formulas. We are on the hunt for a highly skilled Machine Learning Scientist with a creative edge and a strong academic background in machine learning.
The candidate is expected to have a strong mathematical foundation and be capable of analyzing and developing novel models. The selected candidate will be instrumental in inventing and designing new deep learning models, including graph neural networks that predict chemical properties of battery materials and approximate density function theory calculations (DFT). Additionally, this role involves pioneering developments in generative models using reinforcement learning, variational autoencoder (VAE), generative adversarial networks (GANs), and stable diffusion models for the generation of novel battery materials.
You will be required to collaborate with strong academic labs, engaging in machine learning research aimed at addressing our battery design challenges and enhancing our systems' ability to understand and interpret data-driven science efficiently. Your contributions will be instrumental in enhancing our ability to analyze experimental data and intuitively achieve groundbreaking advancements in battery technology.
This is a remote position.
Responsibilities
- Design and develop advanced deep learning models, including graph neural networks, to accurately predict the chemical properties of battery materials.
- Lead innovative projects in generative model development, utilizing reinforcement learning, VAE, GANs, and stable diffusion models to create new battery material compositions.
- Develop machine learning/deep learning-based models to approximate density function theory calculations (DFT).
- Collaborate with a multidisciplinary team of scientists and engineers to integrate machine learning methods into the battery discovery process.
- Stay abreast of the latest advancements in machine learning and computational chemistry to continuously improve model performance and discovery methodologies.
- Contribute to academic and industry discussions by publishing research findings in top-tier journals and presenting at conferences.
Qualifications
- Advanced degree (Ph.D. preferred) in Computer Science, Artificial Intelligence, Machine Learning, or a related field.
- Strong foundation and proven track record in deep learning, graph neural networks, reinforcement learning, variational autoencoder, generative adversarial networks and diffusion models.
- Have a distinguished history of contributing to the field through publications in leading machine learning conferences and journals, such as ICLR, NeurIPS, ACL, ICML, CVPR, and Nature Machine Intelligence.
- Demonstrated experience in developing and implementing machine learning models for scientific or industrial applications.
- Proficiency in programming languages and frameworks relevant to machine learning such as Python, PyTorch, and TensorFlow etc.
- Exceptional analytical and problem-solving skills, coupled with a strong passion for research and innovation in machine learning.
- Solid understanding of computational chemistry, physics, or material science is a plus.
