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英国拉夫堡大学Intelligent Diabetes Management方向招生
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Intelligent Diabetes Management Diabetes is a global health challenge, affecting more than 590 million people worldwide. Many individuals with diabetes eventually require insulin therapy, but insulin dosing remains highly complex due to substantial variability in insulin sensitivity, glucose dynamics, lifestyle factors, and treatment adherence. Current dosing approaches, such as standard basal titration algorithms, are often rigid, slow to adapt, and insufficiently personalised, leading to suboptimal glucose control and an increased risk of both hyperglycaemia and hypoglycaemia. This project, taking a whole systems approach, seeks to develop and validate an individualised and intelligent adaptive Model Predictive Control (MPC) strategy for insulin dose guidance and diabetes management in people with diabetes. The proposed control system will be architected and designed to update patient-specific parameters online, managing disturbances such as meals and physical activity, and ensuring safety against hypoglycaemia. Key objectives of this project are: (a) to capture personalised glucose–insulin models; (b) to develop adaptive and robust MPC algorithm capable of addressing intra- and inter-day variability in insulin sensitivity and lifestyle factors; (c) to incorporate safety layers and fault-tolerant filtering to manage sensor delays, noise, and uncertainties; and (d) to evaluate proposed controllers through in silico simulations and, where feasible, clinical datasets or collaborations with healthcare partners. This research has the potential to transform insulin therapy in diabetes by enabling intelligent, patient-centred dose guidance systems. The outcomes could inform the next generation of digital health tools, smart insulin delivery devices, and clinical decision-support platforms, ultimately improving quality of life and reducing complications for millions of people living with diabetes. Entry requirements: 1. 1st class Master's degree in Engineering, Mathematics or Physics. 2. Strong background or interest in automation, control engineering, or machine learning techniques. 3. Experience with programming languages, such as Matlab, or Python. English language requirements: Applicants must meet the minimum English language requirements. Further details are available on the International website (https://www.lboro.ac.uk/international/applicants/english/). Funding information: The applicant will be supported to apply for China Scholarship Council PhD studentship. Supervisor Introduction: Chengyuan Liu is a Lecturer in Safety-critical Control for Autonomous systems in the Department of Aeronautical and Automotive Engineering (AAE) at Loughborough University. She received her PhD in Robust Control from the Department of Electrical and Electronic Engineering at Imperial College London, in 2017. Following that, she worked as a Research Associate in the Control of Artificial Pancreas at Imperial College London. From 2019 to 2021, she was a Research Fellow in Intelligent Robotics and Automation at the Centre for Aerospace Manufacturing, University of Nottingham. She has been a lecturer in the AAE Department at Loughborough University since 2021. Her research interests primarily focus on safety-critical control and intelligent control for autonomous systems, especially for autonomous vehicles, artificial pancreas, and industry robots. Apply: Send CV and Transcripts to c.liu@lboro.ac.uk |
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