PhD in Materials Science
Core
Develop machine-learning accelerated simulation methods to understand and optimize interfaces in hybrid organic-inorganic materials for sustainable energy devices.
Role type
PhD researcher in computational materials science (machine learning & molecular dynamics)
Builds
Machine learning potentials and Hamiltonians for ion transport and electronic structure predictions in organic batteries and solar cells
Domain
Materials science / Computational chemistry / Sustainable energy
Deliverable
production ML models
Required skills
Density functional theory, molecular dynamics simulations, machine learning potentials, Hamiltonian learning, organic-inorganic interface analysis
Preferred skills
Fine-tuning machine learning potentials, Hamiltonian learning, molecular dynamics of materials
Technologies
Density functional theory, molecular dynamics, machine learning
Responsibilities
Develop ML potentials and Hamiltonian learning approaches for organic-inorganic interfaces; Perform molecular dynamics simulations of interfacial ion transport; Contribute to design of stable material interfaces for efficient charge and ion transport; Collaborate with experimental and theoretical groups
Seniority
Early-stage researcher (PhD student)