This project aims to develop geometric-algebra artificial-neural foundation models for molecular and biomolecular machine learning and artificial intelligence, using Clifford algebra to represent geometry, orientation, and tensor quantities in a single equivariant feature space. The goal is to build scalable 3D models for physical chemistry and biochemistry tasks, with the hypothesis that geometric algebra yields more expressive and physically consistent representations than current neural networks.
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Job Type
Part-time
Career Level
Entry Level
Education Level
No Education Listed