Hewlett Packard Labs -- Physics-Based Generative AI Research Associate (Intern) (Open)
Job Description
Project description:
While generative AI models, such as Large Language Models (LLMs) and diffusion models, have reached unprecedented results, but they lack interpretability and are inefficient, especially when involved in complex reasoning and scientific quests. In the Emergent Machine Intelligence Team at Hewlett Packard Labs, we are dedicated to pushing the boundaries of what's possible with artificial intelligence. We specialize in creating cutting-edge machine-learning algorithms and applications by augmenting state-of-the-art LLMs with concepts and tools from symbolic AI, physics simulators, statistical physics, and non-equilibrium thermodynamics. We aim to revolutionize AI by creating first-principled generative models for system-2 thinking that can perform reliable and interpretable complex reasoning over multimodal data while delivering efficient hardware performance. Join us to be a part of a team that shapes the future of high-performance AI.
*Hiring for multiple positions*
Required skills and experiences:
- Currently pursuing a PhD in Computer Science, Artificial Intelligence, Machine Learning, Physics, Electrical Engineering, Mathematics, or other related fields.
- Proficiency in Python
- Familiarity with AI/ML frameworks (e.g., TensorFlow, PyTorch).
- Excellent understanding of neural networks, deep learning, and generative models.
- Excellent written and verbal communication skills
- Ability to collaborate effectively across diverse teams
Desired skills and experiences
- Background in computational physics, with an understanding of how physical principles can be applied to improve machine learning models and algorithms
- Familiarity with combinatorial optimization techniques and algorithms, and their application in solving complex computational problems.
- Experience with energy-based models
- Experience with diffusion models
- Experience in applying generative AI models to solve problems in areas such as material science, quantum computing, or complex system simulations.
We are looking for candidates who are working towards a PhD degree in Computer Science, Artificial Intelligence, Machine Learning, Physics, Electrical Engineering, Mathematics, or other related fields. The ideal candidate will have research experience in the areas of generative AI, statistical physics, or probabilistic computing. Applicants should be comfortable working in a strongly collaborative and multidisciplinary industrial basic research environment.