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, advanced statistical methods and the potential to develop pioneering reconstruction and calibration techniques involving machine learning. The PhD will prepare equally well for a career in industry and
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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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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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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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of acoustic wave propagation in moving fluid and physics-based machine learning (ML) methods. Support experimental design in the laboratory, carry out data processing and to use the experimental results
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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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contribute to advancing simulation-based testing methods for ADS. You will contribute to cutting-edge research projects, including the EPSRC-funded SimpliFaiS: Simplification of Failure Scenarios for Machine
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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