Research study opportunities and scholarships
Find out about research opportunities at the Centre for Advanced Computational Science, including scholarships and studentships.
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Current opportunities
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Self-funded PhD: Using Computational Science to Understand the Underlying Mechanisms of Coexistent Atrial Fibrillation and Heart Failure
- Closing date: None
Project Summary
Atrial Fibrillation is the most common arrhythmia. Heart Failure has a prevalence of over 23 million worldwide. Both conditions usually coexist giving a worse prognosis for the patient.
The project aims to elucidate pathophysiological mechanisms of coexistent AF and HF, and to use models predictively to explore potential therapeutic strategies.
Specific Requirements of the Project
- A very good undergraduate degree (at least a UK 2:1 honours degree, or its international equivalent) in a quantitative discipline such as engineering, physics, computer science or mathematics.
- A good mathematical background and programming skills in at least one of C/C++, Rust, Nim, Julia or Python.
- Experience of numerical methods and machine learning will be beneficial.
- A keen interest in high-impact research work at the interface of physics, engineering, computer science and medicine.
Project Aims and Objectives
Atrial fibrillation (AF) and Heart Failure (HF) frequently co-exist in patients resulting in a worse prognosis compared to either condition existing alone. AF represents the most common arrhythmia of HF patients (approximately 13% of patients in the age range 35 to 64; and 21% of patients aged 65 years or older). On the other hand, HF is a major promoter of AF, increasing the risk of developing AF by approximately five-fold. Existing treatment strategies are frequently ineffective and this is likely to result from poor understanding of the underlying aetiologies. Integrated multiscale computational modelling and Artificial Intelligence offer an important approach to incorporate multiple changes in these conditions and elucidate arrhythmia mechanisms.
The mechanism(s) underlying the genesis of AF in HF patients and HF in AF patients remains unclear. This project proposes to investigate and elucidate the underlying pathophysiological mechanisms of this duality and potential therapeutic strategies. This will be done by developing novel AI-enabled electromechanical (as opposed to the usual electrophysiology only) cellular, tissue and organ models for these conditions.
- Contact: Dr I Adeniran
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Self-funded PhD: Identifying physiological parameters for AI-enabled biophysical modelling of skeletal muscle to better understand physical fatigue
- Closing date: None
Fatigue is often referred to as an overwhelming sense of exhaustion or tiredness and lack of energy. It is a complex phenomenon, classified in terms of duration and effects (cognitive/physical).
It is a common and debilitating, non-specific symptom of many neurological, musculoskeletal and cardiovascular conditions, and suffered by cancer and stroke survivors, and those recovering from viral infections. It also increases with ageing and frailty.
Despite the large number of health conditions accompanied with fatigue, there are currently no official treatment recommendations, except self-managed lifestyle changes.
This makes it an important problem for which new treatment approaches will have significant health benefits. It has recently been suggested that interactions between status and functioning of several physiological systems underpin chronic fatigue. This and the lack of treatment options reflects the complex and multi-factorial nature of fatigue, highlighting it as a problem requiring multiscale and multimodal research approaches to solve.
Our overarching aim is to:
- Elucidate physiological mechanisms underlying physical fatigue using AI-enabled, physiology-informed biophysical modelling.
Drawing on our expertise, the proposed research focuses on physical fatigue and potential issues within the neuro-motor system by developing biophysically detailed computational and mathematical models for each subcellular component of the skeletal muscle system.
- Contact: Dr I Adeniran, Dr E Hodson-Tole and Professor H Degens
Funding
