Doctoral (PhD) student in Machine Learning and Image Processing for Quantitative Magnetic Resonance Imaging - Hiring in process/Finished, not possible to apply

Do you want to contribute to top quality medical research?

To be a doctoral student means to devote oneself to a research project under supervision of experienced researchers and following an individual study plan. For a doctoral degree, the equivalent of four years of full-time doctoral education is required.

The research group

The Department of Molecular Medicine and Surgery (MMK) and the Cardiovascular Magnetic Resonance (CMR) Group is looking for a Ph.D. student to join Dr. David Marlevi and his team to develop dedicated machine learning and physics-informed image analysis tools to improve assessment of cardiac hemodynamics using four-dimensional flow magnetic resonance imaging (4D Flow MRI). Based in a clinically integrated, well-established larger network on cardiovascular imaging, and as part of a broad-reaching European research initiative (https://academic.oup.com/eurheartj/advance-article/doi/10.1093/eurheartj/ehad062/7046109?login=false), you will join a research team exploring and evaluating dedicated data-driven imaging networks and processing algorithms to expand the use of 4D Flow MRI into previously inaccessible domains, and improve our understanding of complex cardiac disease through a combination of experimental and clinical studies.

The doctoral student project and the duties of the doctoral student

Four-dimensional flow magnetic resonance imaging (4D Flow MRI) has emerged as a powerful imaging technique providing unique full-field mapping of blood flow to gain novel insights into cardiovascular disease. However, the extensive acquisition limits effective spatiotemporal coverage, hindering assessment of complex diseased anatomies or transient flow events. Nevertheless, the incorporation of machine learning and advanced image analysis is starting to fundamentally change what we can analyse and resolve. Specifically, our group has shown how the use of super-resolution networks and physics-informed image processing now allow for usage of quantitative 4D Flow MRI in previously inaccessible domains, opening for novel diagnostic paradigms.

As a Ph.D. student, you will continue these advancements by exploring and implementing novel machine learning architectures for enhanced quantification of 4D Flow MRI data. Additionally, by coupling these developments to physics-informed image analysis, you will be using 4D Flow MRI to provide novel insights into complex hemodynamic behavior, with specific focus on improved cardiac care. Based in a highly translational research environment, the Ph.D. project specifically seeks to (1) utilize advanced machine learning techniques to enable spatiotemporal super-resolution 4D Flow MRI; (2) couple these findings to developments in physics-informed image analysis to validate recovery of intracardiac pressure gradients from 4D Flow MRI; and (3) investigate how super-resolution intracardiac pressure gradients expands our understanding of complex cardiac disease. You will have access to state-of-the-art MRI systems and computational resources, and will directly deploy and evaluate novel research developments in a direct clinical environment.

As part of an individual research project, you will be responsible for setting up and evaluating computational and benchtop experiments, defining optimal patient studies, and write scientific articles to review and report the results of these experiments.

Your profile

We are looking for a highly motivated, independent, and analytical person, with a documented background in scientific machine learning, data science, engineering physics, medical physics, or similar. Specific knowledge of biomedical engineering, or application of machine learning or image analysis for medical image purposes is advantageous, but not required. A solid and documented experience from scientific programming in Python, including machine learning libraries such as TensorFlow or PyTorch is required, and experience in high-performance computing and workload management for computational analysis is seen as a major advantage. Experience with experimental MRI studies or benchtop imaging phantoms is meritorious, but not required. Previous experience with scientific data analysis is advantageous, with involvement in previous scientific publications meritorious.  

Further, great emphasis will also be placed on personal competence, with an ambitious, systematic, and problem-solving attitude being central to the sought-after candidate. Excellent communication skills in both spoken and written English are a must, as is the ability to interact and work in a translational research team on national or international level. Dedication to open science is also central, with group work focused on open-source developments, data sharing, etc.

What do we offer?

As part of the Cardiovascular Magnetic Resonance Group, we offer a unique translational research environment with wide-ranging expertise spanning clinical science, biomedical engineering, and medical physics, all working together to improve cardiovascular disease diagnostics. With the position part of a recently funded broad-reaching European research initiative for advanced hemodynamic imaging (the MultiPRESS project: https://academic.oup.com/eurheartj/advance-article/doi/10.1093/eurheartj/ehad062/7046109?login=false), the student will also be directly involved in cutting-edge data-driven image science, collaborating with international networks to push learned and physics-driven imaging into novel application areas. Herein, participation in international workshops and conferences, as well as potential research visits to collaborating sites will be included in the expected work. Further, being deeply integrated with the clinical activities at the Karolinska University Hospital, direct access to state-of-the-art imaging equipment, diverse patient cohorts, and relevant computational resources provide excellent opportunities for ground-breaking research.

On a larger scale, Karolinska Institutet is one of the world’s leading medical universities, with the vision to provide better health for all. As a doctoral student you will be offered an individual research project, a well-educated supervisor, a vast range of elective courses, the opportunity to work in a leading research group, and explore opportunities for international exchange. You will be employed on a doctoral studentship which means that you receive a contractual salary. Employees also have access to our modern gym for free and receive reimbursements for medical care.

Eligibility requirements for doctoral education

In order to participate in the selection for a doctoral position, you must meet the following general (A) and specific (B) eligibility requirements at latest by the application deadline.

It is your responsibility to certify eligibility by following the instructions on the web page Entry requirements (eligibility) for doctoral education.

A) General eligibility requirement

You meet the general eligibility requirement for doctoral/third-cycle/PhD education if you:

1. have been awarded a second-cycle/advanced/master qualification (i.e. master degree), or
2. have satisfied the requirements for courses comprising at least 240 credits of which at least 60 credits were awarded in the advanced/second-cycle/master level, or
3. have acquired substantially equivalent knowledge in some other way in Sweden or abroad.*

Follow the instructions on the web page Entry requirements (eligibility) for doctoral education.

*If you claim equivalent knowledge, follow the instructions on the web page Assessing equivalent knowledge for general eligibility for doctoral education.

B) Specific eligibility requirement

You meet the specific eligibility requirement for doctoral/third-cycle/PhD education if you:

- Show proficiency in English equivalent to the course English B/English 6 at Swedish upper secondary school.

Follow the instructions on the web page English language requirements for doctoral education.

Verification of your documents Karolinska Institutet checks the authenticity of your documents. Karolinska Institutet reserves the right to revoke admission if supporting documents are discovered to be fraudulent. Submission of false documents is a violation of Swedish law and is considered grounds for legal action.

(A) and (B) can only be certified by the documentation requirement for doctoral education.

Terms and conditions

The doctoral student will be employed on a doctoral studentship maximum 4 years full-time.

Application process

Submit your application and supporting documents through the Varbi recruitment system. Use the button in the top right corner and follow the instructions. We prefer that your application is written in English, but you can also apply in Swedish.

Your application must contain the following documents:

- A personal letter and a curriculum vitae
- Degree projects and previous publications, if any
- Any other documentation showing the desirable skills and personal qualities described above
- Documents certifying your general eligibility (see A above)
- Documents certifying your specific eligibility (see B above)

Selection

A selection will be made among eligible applicants on the basis of the ability to benefit from doctoral education. The qualifications of the applicants will be evaluated on an overall basis.

Karolinska Institutet uses the following bases of assessment:

- Documented subject knowledge of relevance to the area of research
- Analytical skill
- Other documented knowledge or experience that may be relevant to doctoral studies in the subject.

All applicants will be informed when the recruitment is completed.

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