**Job Description**
This position offers an opportunity to advance research in cutting-edge generative and predictive machine learning methods for materials discovery. The role is a fully funded postdoctoral researcher position at DTU Compute, focusing on the “AI-driven materials optimization for light trapping in thin-film solar cells” project. The researcher will develop neural diffusion techniques for designing materials with targeted optical properties and create multi-fidelity predictive models using techniques like equivariant graph neural networks with tensor embeddings, all within a closed-loop framework for iterative improvement between generative design and predictive modeling.
**Skills & Abilities**
• Proven experience with graph neural networks, transformers, or similar neural network methods applied to materials or molecules.
• Proven experience with implementing machine learning methods in Python and Pytorch as well as deployment on GPU clusters.
• A strong publication record in graph neural networks, geometric deep learning, representation learning, machine learning-based molecular/materials discovery, diffusion-based generative models, or other related fields.
• High level of motivation and creative problem-solving skills.
• Excellent communication and writing skills in English.
**Qualifications**
Required Degree(s):
• Ph.D
**Experience**
Other:
• Proven experience with graph neural networks, transformers, or similar neural network methods applied to materials or molecules.
• Proven experience with implementing machine learning methods in Python and Pytorch as well as deployment on GPU clusters.
• A strong publication record in relevant fields.
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