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mathematics is essential. Prior experience with simulation tools or microstructural modelling is desirable. To apply, please contact the supervisor, Prof Andrey Jivkov - andrey.jivkov@manchester.ac.uk . Please
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: This PhD project will develop model- and data-driven hybrid machine learning material models that capture the complex, nonlinear, path- and history-dependent behaviour of materials. The goal is to create
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least one of areas like animal communication research, probabilistic modeling, or language evolution is a strong requirement. As the position involves computational / mathematical modeling in the form
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Research Studentship in ‘Deformation and fracture of TRISO fuel particles’ 3.5-year DPhil studentship Supervisor: Prof Dong Liu, Prof Emilio Martinez-Paneda About the Project The proposed PhD
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approach in the Arctic Ocean involves the artificial thickening of sea-ice to prevent total loss during the summer melt season. The PhD candidate will work closely with biogeochemical (BGC) modellers and
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civil/electrical/control engineering or mathematics or related study programs with a solid basis in choice modelling and/or reinforcement learning, with knowledge of MATSim is advantageous. Description
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respect and academic freedom tempered by responsibility. The project will be supervised by Assoc. Prof. Thomas Christensen. As part of the project, the PhD student is expected to interact with and visit
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– from the modeling of material behavior to the development of the material to the finished component. PhD Position in Machine Learning and Computer Simulation Reference code: 50145735_2 – 2025/WD 1
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of models in existing simulation software conducting numerical studies, also on HPC systems Further specific tasks can be tailored to the attitude and interests of the PhD students/postdocs. Requirements
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of the STAMP RSV Program, supported by the Stan Perron Charitable Foundation. The PhD candidate will play an important role in developing models of RSV transmission and vaccination efficacy to inform