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Field
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data gaps by combining process simulation (e.g., Aspen software) with machine learning techniques. By developing accurate, large-scale life cycle inventory data using enhanced digital tools like deep
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Hangsterfer's Laboratories) EPSRC Centre for Doctoral Training in Machining, Assembly, and Digital Engineering for Manufacturing PhD Research Project Directly Funded UK Students Dr Thawhid Khan, Prof Hassan
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-resolution imaging and reconstruction of neural tissues (see https://ist.ac.at/en/research/siegert-group/). Leveraging computational tools such as machine learning and topological data analysis, we will
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holder will work closely with external partners and members of the EU consortium, and will focus on developing machine learning applications to investigate the implications of genetic variation within
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Shifting the paradigm: machine-assisted scholarly digital editing Digital Humanities Institute PhD Research Project Self Funded Dr Isabella Magni Application Deadline: Applications accepted all year
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PhD student will expect to develop some experience in developing power systems models using a range of computer languages and tools (e.g. Python, MATLAB, OPNET, etc), ideally for applications involving
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Machine tool dynamics-based digital twins for real-time monitoring of cutting tool conditions in smart manufacturing School of Electrical and Electronic Engineering PhD Research Project Self Funded
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main project by addressing specific case studies or specific targeted techniques. The main tools to be used will come from the discipline of Machine Learning, particularly those based on Bayesian methods
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Early-stage failure prediction in fusion materials using machine learning CDT in Developing National Capabilities for Materials 4.0 PhD Research Project Directly Funded UK Students Prof Christopher
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. Degree in Infocomm, Computer Science, Cyber Security, Computer/Electrical Engineering, Information Technology or equivalent. Possessing a Master’s or PhD degree will be advantageous. Strong interest and