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greener transportation and energy. Building on recent advances, the successful candidate will use a powerful combination of dynamical systems theory, optimisation, DNS and machine learning to model and
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Knowledge of workshop activity: for example vacuum technology, metrology, material finishing, CNC and conventional machining, welding (particularly TIG). Desirable Application/interview Experience with
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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 Students Worldwide Dr Thawhid Khan, Prof
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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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experience of treatment. The overarching aim of the project is to use machine learning methods to understand why many people who are referred for treatment will drop out prematurely. To do this, two studies
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who is also skilled in bioinformatics, image analysis, and machine learning. You’ll be part of a dynamic, supportive, and forward-thinking research environment committed to making real clinical impact
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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