The positions offer inspiring and motivating tasks in an international and inclusive environment characterised by open, committed and engaged researchers, both at the university and among the industry employees. You will be working in close collaboration with our industry partners, aiming for the research to become innovative solutions that will be used in practise in the future.

The PhD projects form a range of highly relevant and innovative topics in medical, maritime and oil & gas applications, and will employ theory and methods from physics and acoustics, signal and image processing and artificial intelligence (AI)/machine learning.

You will work with applications for more accurate and efficient imaging of the human heart, improved non-destructive testing using ultrasound, novel characterization of heart muscle fibrosis using methods enabled by our state-of-the-art ultrasound equipment, automatic classification of the sea bed using SONAR, and flow monitoring and measurement in both oil wells and the human heart. It is an explicit aim in CIUS that we seek synergies between all application areas.

Duties of the position

1. Increased sensitivity and detailed diagnosis of corroded oil and gas pipes using ultrasound

2. Advanced processing of data from existing well logging tools

3. Ultrasonic doppler measurements while drilling for identification and quantification of influx/efflux in boreholes

4. Ultrasound and machine learning for automated monitoring of maturation states in Atlantic salmon

5. Machine learning for seabed classification in SONAR

6. Artificial intelligence for improved echocardiography and its integration to the electronic health journal

7. Non-invasive measurement of cardiac valve leakage jets

8. Non-invasive (electro) mechanical wave imaging in echocardiography

Required selection criteria

The PhD-position's main objective is to qualify for work in research positions. The qualification requirement is that you have completed a master’s degree or second degree (equivalent to 120 credits) or equivalent education with a grade of B or better in terms of NTNU’s grading scale. If you do not have letter grades from previous studies, you must have an equally good academic foundation. If you are unable to meet these criteria you may be considered only if you can document that you are particularly suitable for education leading to a PhD degree.

• A master’s degree or equivalent education from acoustics, physics, mathematics, signal processing, electronics, computer science, engineering, maritime engineering or petroleum engineering with a weighted-average grade of B or higher, in accordance with NTNU’s grading system

• Good English skills, spoken and written

The successful candidate holds an MSc degree with emphasis on theory and methods from acoustics, (bio)physics, mathematics, signal processing, electronics, or computer science. CIUS aims to be a world-leading centre for research and innovation in next-generation ultrasound technologies. Candidates for this position will be selected in accordance with this aim.

Good analytical and programming skills are important. For some projects, it is imperative that the candidate has a proven ability to work in an experimental setting and is willing and interested in doing laboratory work. Besides technical skills, we are looking for a curious, ambitious candidate who is highly self-motivated to do research and contribute to the innovative work carried out in CIUS. Good communication skills in both oral and written English are expected (see details on formal English requirements below).

The application is to be accompanied by a cover letter explaining how your background and education fit the goals and requirements of the selected project(s) from the list above. Please mark specifically in the application which project(s) you are most interested in.

Preferred selection criteria

• Experience within the field of ultrasound in either medicine or industrial applications

• Experience with methods and / or theory for one of the specific projects listed

• Prior written, scientific output with relevance