This project aims to improve the assessment of diastolic function by combining echocardiographic measurements with patient-specific digital twin technology. The doctoral candidate will develop mathematical models that link clinically acquired echocardiographic data to the underlying physiological and mechanical properties of the heart. By integrating imaging information with computational models, the project seeks to estimate clinically important parameters that are otherwise difficult to measure without invasive procedures.
While the main focus of the project is the integration of conventional echocardiographic measurements with computational models of cardiac function, there will also be opportunities to investigate emerging ultrasound modalities such as blood speckle tracking (BST). BST provides information about intracardiac flow dynamics and can be used to derive parameters such as intraventricular pressure gradients, energy loss, and flow vortices. The clinical utility of these measurements and their relationship to filling pressures and diastolic function will be explored.
The candidate will participate in the collection and analysis of animal and clinical datasets, including echocardiographic studies with simultaneously recorded invasive pressure measurements. These data will be used to develop, calibrate, and validate the proposed modelling approaches. Ultimately, the project aims to create more accurate and clinically useful tools for diagnosing and monitoring heart failure, supporting earlier intervention and more personalised patient care.
Planned Secondments
- King's College London, United Kingdom (2 months): training in advanced cardiac digital twin and heart modelling technologies.
- Maastricht University, Netherlands (1 month): training in the CircAdapt cardiovascular modelling platform and model personalisation methods.
Where to apply
Website: https://2411.webcruiter.no/Main2/Recruit/Public/5167521419
Requirements
Research Field: Engineering » Biomedical engineering
Education Level: Master Degree or equivalent
Specific Requirements
We seek a motivated, creative, and enthusiastic candidate with a strong interest in interdisciplinary research at the interface of engineering, mathematics, and medicine.
- Applicants should hold a Master's degree (or equivalent) in Cybernetics, Electrical Engineering, Informatics, Physics, Mathematics, Biomedical Engineering, Medical Technology, or a related discipline.
- A strong academic record is required with a weighted average grade of B or higher.
- Experience with scientific programming (e.g., Python, MATLAB, or similar languages) is advantageous.
- Knowledge of mathematical modelling, computational mechanics, or data science is considered an advantage.
- Interest in cardiovascular physiology, echocardiography, medical technology, or computational medicine is desirable.
- Experience with experimental, pre-clinical, or clinical research, including data collection, validation studies, or analysis of biomedical data, is an advantage.
- Previous research experience, including scientific publications, conference presentations, or research projects, is beneficial.
- Excellent written and oral communication skills in English are required.
- The successful candidate should be able to work independently while also contributing effectively within a multidisciplinary research team.
- MSCA Mobility Rule: You must not have lived or worked in Norway for more than 12 months in the 3 years before recruitment.
- MSCA Eligibility Rule: You must not already hold a doctoral degree and must be eligible to enrol in the PhD programme at the University of Oslo.
Languages: English
Level: Excellent
Additional Information
Website for additional job details: https://www.cdtnet.eu/
Work Location(s)
Number of offers available: 1
Company/Institute: Oslo universitetssykehus HF, Rikshospitalet
Country: Norway
City: Oslo
Postal Code: 0372
Street: Sognsvannsveien 20
Contact
City: Oslo
Website: https://www.ous-research.no/icf/
E-Mail: espen.remme@medisin.uio.no
CDTnet@kcl.ac.uk
