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Physics informed learning for high fidelity medical simulators School of Electrical and Electronic Engineering PhD Research Project Self Funded Prof Sanja Dogramadzi Application Deadline
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elicited directly from the participating stakeholders within the decision support process. However, these stakeholder preferences may or may not align with the social preferences that members of the public
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with a first class or upper second-class degree in engineering, physics, applied mathematics or a related field. A solid foundation in fluid dynamics and heat transfer, and experience with computer
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to the additive manufacture of an aluminium alloy. Develop melt pool physics simulations of the additive manufacturing process for the industry-based aluminium alloy. Develop computational methods for quantifying
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expectation to contribute to scientific publications and demonstrations. Support will be provided by senior colleagues in the Digital Manufacturing Laboratory. You will have completed a First degree in Computer
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methods such as graph theory and networked systems, with the latest AI-enable data fusion and digital co-simulation technologies for a more resilient cyber-physical power systems, so that cyber can have a
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transport modelling and process-based environmental risk assessment. Numerical simulation techniques for hydrogeological systems. Advanced uncertainty quantification to improve model robustness. Scientific
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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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techniques, to support predictive, process and product engineering. Modelling tools to be used will vary according to the application but are likely to including process simulation using Population Balance
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/interview) Experience of using computer-aided engineering including PCB design, schematic capture, embedded programming and circuit simulation (assessed at application/interview) Evidence of working