Intelligent Cardiovascular Digital Twin And Adaptive Control Phd

This PhD opportunity at Loughborough University centers on the creation of an intelligent cardiovascular digital twin coupled with an adaptive control system that relies solely on electrocardiogram (ECG) signals for real‑time cardiac regulation.

The project addresses a critical gap in current cardiovascular monitoring, where simplified models fail to capture individual physiological variability, limiting the effectiveness of diagnostics and therapeutic interventions.

By integrating nonlinear dynamical modelling of the sinoatrial (SA), atrioventricular (AV), and His‑Purkinje (HP) nodes with modern machine‑learning techniques, the candidate will develop patient‑ and athlete‑specific virtual hearts that continuously update their parameters from incoming ECG data.

Key research activities:

• Design and develop personalized cardiovascular digital twins based on detailed nonlinear physiological models.
• Pioneer ECG‑based parameter identification and adaptive model refinement using data‑driven and machine‑learning approaches.
• Engineer adaptive output‑feedback control strategies that use only ECG measurements for real‑time regulation of heart rate and contractility.
• Implement observer‑based estimation methods to cope with physiological uncertainty and unmeasurable states.
• Validate the framework through a rigorous progression: in‑silico simulation → Processor‑in‑the‑Loop (PIL) → Hardware‑in‑the‑Loop (HIL) testing.
• Extend the digital twin to sport‑performance applications, enabling workload optimisation, fatigue detection, and recovery monitoring.
• Disseminate findings in leading international journals and conferences, contributing to the fast‑growing field of digital health.

The work sits at the intersection of control engineering, biomedical engineering, and data science, with direct implications for intelligent pacemaker control, wearable health monitors, and athletic performance optimisation.

Loughborough University’s strong research environment—94% of its impact rated world‑leading or internationally excellent in REF 2021—provides access to state‑of‑the‑art laboratories, high‑performance computing facilities, and a vibrant community of researchers in mechanical, electrical, and manufacturing engineering.

Successful candidates will be supervised by Dr. Behnaz Sohani, an expert in control systems and biomedical engineering, and will receive guidance through regular supervisory meetings, progress reviews, and opportunities to present at group seminars and national workshops.

The PhD is offered as a self‑funded position; tuition fees for the 2026‑27 academic year are £5,238 for UK students and £29,500 for international students per annum, with start dates available in October 2026, February 2027, or July 2027.

Applicants should hold a relevant master’s degree (or equivalent) in engineering, biomedical engineering, computer science, or a closely related discipline, and demonstrate strong analytical skills, programming experience (e.g., MATLAB/Python), and an interest in physiological modelling or control theory.