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, through specific experimental arrangements during the PhD project. This PhD is fully funded by the University of Manchester as part of their commitment to support a recently successful BBSRC-Arxada award
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(photocatalysis) and will give the candidate experience in a wide range of skills relevant to sustainable chemistry and biomanufacturing at the interface of academia and industry. Students with a strong background
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mathematics, programming, and machine learning. *Interest or experience in wireless communications, signal processing, or 6G technologies. To apply, please contact the main supervisor; Dr. Zahra Mobini
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challenges in engineering Desirable: Experience with mathematical modelling, optimisation techniques, or supply chain analysis Background knowledge in bio-based materials, biorefineries, or circular economy
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contact the main supervisor Dr Zhipeng Wang - zhipeng.wang@manchester.ac.uk . Please include details of your current level of study, academic background and any relevant experience and include a paragraph
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any relevant experience and include a paragraph about your motivation to study this PhD project.
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that experiments have historically been conducted without precise control over the environmental conditions, allowing too many variables to influence tree growth simultaneously. For decades, the field
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supervisor; Dr Gholinia - ali.gholinia@manchester.ac.uk . Please include details of your current level of study, academic background and any relevant experience and include a paragraph about your motivation
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current level of study, academic background and any relevant experience and include a paragraph about your motivation to study this PhD project.
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. Fe, S) on CNT purity and structure. Evaluate CNTs as conductive additives in standard Li-ion battery electrodes. Apply AI/machine learning to optimise experimental design and growth parameters