Arash Rabbani

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A fully funded PhD position is available at the University of Leeds, focusing on the integration of artificial intelligence, geophysics, and remote sensing for sustainable mineral exploration. The successful candidate will join an interdisciplinary team and work on developing innovative methodologies for mineral prospectivity mapping using machine learning, specifically attention-based convolutional neural networks. The project will utilize multi-source geophysical data, including gravity, magnetic, radiometric, and hyperspectral remote sensing, to identify promising mineral exploration targets. Real-world case studies in the UK will be used to develop robust and environmentally sustainable exploration strategies. The position is funded through the YES DTN (Yorkshire Environmental Sciences Doctoral Training Network) and covers tuition, stipend, and research costs for 3.5 years. Applicants should have a strong background in geology, geophysics, or earth sciences (minimum 2:1), good numerical skills, and an interest in machine learning. Both UK and international students are eligible. Applications open on 17 November 2025 and close on 7 January 2026. For more information and to apply, visit the provided link or contact the supervisors via email.

Industrial PhD studentship in AI and Powder Metallurgy at the University of Leeds , jointly hosted with Plasee Austria . This is a fully funded PhD opportunity at the intersection of machine learning , computer vision , powder processing , and materials science , focused on developing AI-driven models to predict and optimise the flow and compaction behaviour of refractory metal powders such as Tungsten , Molybdenum , and Tantalum . The studentship is interdisciplinary and involves the School of Computer Science and the School of Chemical and Process Engineering at Leeds, with direct industrial engagement through Plasee Austria’s R&D team. The post is announced by Arash Rabbani , Assistant Professor in Computer Science (AI for Materials). Funding: full academic fees plus a tax-free stipend of £21,805/year for 3.5 years (EPSRC rate), plus a conference travel budget. Eligibility: applicants should have a strong background in data science , machine vision , or a related quantitative discipline, with an interest in engineering or materials science applications. Laboratory experience is favourable. The opportunity is for UK/home-fee eligible students only . Start date: October 2026. How to apply: contact Arash Rabbani informally by email with your CV and a short statement of interest.