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, and digital approaches using mutual learning, inclusive theory-development and creative methodologies (e.g. filmmaking, VR technologies) The successful candidate will support the Co-Investigator in co
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
. This PhD project will tackle that challenge by developing intelligent methods that combine AI techniques such as language models that interpret technical text and knowledge graphs that map engineering
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will be employed for a period of 6 months and will be required to fulfil the research obligations of a project entitled Development of Hydrogen Hybrid Energy Storage System H2SS Reactor and Advanced
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are suitable. The aims of this project are to Review operating characteristics proposed for rare disease trials Develop novel Bayesian operating characteristics for different types of rare disease trials Apply
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Project details Early detection of cognitive decline allows identification of those at high risk of developing dementia when medical treatments may be effective in preventing disease onset. Our
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-the-counter (OTC) medicines as a source of syndromic surveillance for diseases in the community. N/LAB is a centre of excellence, developing and applying innovative research in Artificial Intelligence (AI) and
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• Experience in developing wave or tidal energy or similar field • Simulation experience in finite element analysis and/or hydrodynamics and/or time domain simulation such as Matlab SIMULINK • Ability
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, working closely with Professor Nora Pashayan. The successful candidate will focus on developing ethnicity-specific risk thresholds that more accurately reflect the variations in breast, ovarian, and
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to talented prospective or mid-study students towards their PhD studies in Research and Development Management. Tenable period Up to three years (pro-rata for part-time students) Continuation of award
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of the complex physics governing the interaction between the heat source and the material. Additionally, it seeks to develop an efficient modelling approach to accurately predict and control the temperature field