CDTnet PhD Fellowship F4: Computational Models for Improved Assessment of Diastolic Function from Echocardiography

Organisation/Company

Oslo University Hospital (OUS)

Department

The Intervention Centre

Research Field

Engineering » Biomedical engineering

Researcher Profile

First Stage Researcher (R1)

Positions

PhD Positions

Application Deadline

30 Sep 2026 - 23:59 (Europe/Brussels)

Country

Norway

Type of Contract

Temporary

Job Status

Full-time

Offer Starting Date

1 Dec 2026

Is the job funded through the EU Research Framework Programme?

Horizon Europe - MSCA

Marie Curie Grant Agreement Number

101312147

Is the Job related to staff position within a Research Infrastructure?

No

Project Description 

Heart failure affects millions of people worldwide, and around half of all cases are linked to problems with the heart's ability to relax and fill properly during diastole. Although echocardiography is the most commonly used imaging technique for assessing cardiac function, many of the key factors responsible for diastolic dysfunction—such as myocardial stiffness, impaired relaxation, and elevated filling pressures—cannot be measured directly using current non-invasive methods. 

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.

Website

https://2411.webcruiter.no/Main2/Recruit/Public/5167521419

Research Field

Engineering » Biomedical engineering

Education Level

Master Degree or equivalent

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

Website for additional job details

https://www.cdtnet.eu/

Number of offers available

1

Company/Institute

Oslo universitetssykehus HF, Rikshospitalet

Country

Norway

City

Oslo

Postal Code

0372

Street

Sognsvannsveien 20

Geofield

City

Oslo

Website

https://www.ous-research.no/icf/

E-Mail

[email protected]
[email protected]

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