Ph.D. Student

**Job Description**
The positions involve research into cutting-edge machine learning methods, focusing on predictive and generative AI for materials. As part of the “AI-driven materials optimization for light trapping in thin-film solar cells” project, students will advance neural network-based methods for materials discovery, develop neural diffusion techniques for designing materials with targeted optical properties, and create multi-fidelity predictive models using equivariant graph neural networks with tensor embeddings. The aim is to train these methods in a closed-loop framework to enable iterative improvement and seamless feedback between generative design and predictive modeling.

**Skills & Abilities**
• Demonstrated experience in deep learning, preferably including some exposure to graph neural networks or geometric deep learning.
• Proven experience with implementing machine learning methods in Python and Pytorch.
• Familiarity with materials physics (a plus).
• High level of motivation and creative problem-solving skills.
• Excellent communication and writing skills in English.

**Experience**
Other:
• May apply prior to obtaining a master’s degree but cannot begin before having received it.

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