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interested in connecting spatial and spectral information to understand complex materials systems at the molecular level with machine learning. PhD Student A will work with tumour sections to develop multiple
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PhD project to develop theoretical computations and modelling methods to guide and optimise the synthesis conditions and composition of electrocatalysts. PhD project to develop theoretical
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Sustainable management of organic wastes is critical for greenhouse gas emissions reduction and circular resource recovery. Organics management via biological and thermal treatment can offer intensive approach to maximise resource recovery and minimise contaminants transfer into the environment....
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In the PhD program, you will be involved in working with a multi-disciplinary team. You will be conducting experiments and developing phenomenological models to understand the graphite formation process from biomass. In the PhD program, you will be involved in working with a multi-disciplinary...
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materials such as metal-organic frameworks and/or experience in direct air capture of carbon dioxide are encouraged to apply. Candidates with synthesis of porous materials such as metal-organic frameworks and
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publication Strong programming skills and familiarity with machine learning or finite element modelling Not currently receiving another scholarship of equal or higher value Application process Future student
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are developing new composites to address the Waste Heat problem. Waste Heat is a huge challenge in the global energy transition. We are developing new materials and system to enhance the conversion of heat to
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following cycles caused by changes in chemistry and hydrogen bond donor (HBD) composition. DES also exhibits poor chemical stability at higher operating conditions, resulting in solvent loss. Furthermore
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PhD Scholarship in the Centre for Advanced Defence Structures and Materials Experimentation (CADSME)
structures and/or materials experimentation. The project would suit students with a background/interest in advanced structures and/or materials experimentation.
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finite element methods, which demand extensive data and are costly, PINNs embed governing physical laws directly into the learning process. This allows effective management of limited and noisy data