PhD Studentship (m/f/x) in Geometric Deep Learning
Core
Conduct research on generative models for crystalline materials using differential geometry and group theory to design novel materials for batteries and carbon capture.
Role type
PhD researcher in geometric deep learning
Builds
Generative models (diffusion models, flow matching) for materials science
Domain
Materials science + Geometric Deep Learning
Deliverable
research
Required skills
Machine learning theory, Linear algebra, Programming, Differential geometry, Representation theory, Mathematical optimization
Preferred skills
Knowledge of symmetry groups in crystal grids
Technologies
Diffusion models, Flow matching
Responsibilities
Develop generative models for crystalline materials, Publish research at major conferences, Collaborate with consortium members, Supervise research interns and students
Seniority
PhD Candidate
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