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. Education and Experience Requirements The entirety of the appointment must be conducted within the United States. Applicants must be: o Currently enrolled in undergraduate or graduate studies at an accredited institution. o Graduated from an accredited institution within the past 3 months; or o Actively enrolled in a graduate program at an accredited institution. Must be 18 years or older at the time the appointment begins. Must possess a cumulative GPA of 3.0 on a 4.0 scale. If accepting an offer, candidates may be required to complete pre-employment drug testing based on appointment length. All students remain subject to applicable drug testing policies. Must complete a satisfactory background check.
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Job Type
Full-time
Career Level
Intern
Education Level
No Education Listed
Number of Employees
1,001-5,000 employees