Chalmers and the University of Gothenburg is currently offering a PhD student position focusing on generative AI. This PhD position is part of the WASP-WISE NEST project RAM³ – a multidisciplinary research effort at the intersection of machine learning and materials science. The project brings together PhD students and senior researchers from multiple disciplines to tackle challenges in sustainable aluminium through AI-driven microstructural analysis.
Project description
This PhD project is part of the interdisciplinary WASP-WISE NEST project RAM³, which aims to enable the use of recycled aluminium in high-performance applications through machine learning, computer vision, and materials science.
The focus of this position is on developing AI methods for automated microstructure analysis and 3D microstructure generation. By combining self-supervised learning and diffusion-based generative models, the goal is to:
- Reconstruct high-resolution 3D microstructures from microscopy data
- Learn meaningful representations of complex material structures
The work contributes to both scientific understanding and sustainable industrial innovation.
Main responsibilities
Your main responsibility is to pursue your own doctoral studies. You are expected to:
- Develop your own scientific concepts
- Communicate the results of your research verbally and in writing, in English
The position generally also includes teaching at the undergraduate level or performing other duties corresponding to 20% of full-time employment.
Workplace description
The Computer Vision Group at the division of Signal processing and Biomedical Engineering conducts leading research in image analysis, computer vision, and machine learning, with a growing emphasis on generative AI and AI for scientific discovery. Our mission is to develop intelligent systems that can learn to interpret complex visual and scientific data, enabling breakthroughs in areas such as autonomous navigation, medical imaging, and materials science. The research group is recognized for its strong theoretical foundation, practical impact, and close collaborations with academic and industrial partners.
