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Cardiologie Erasmus MC

PhD candidate in the new ICAI lab Trustworthy AI for Magnetic Resonance Imaging: project on End-to-end deep learning quantitative MR reconstruction

  • Closing date 30-01-2023
  • 52.06.22.TDA
  • 36 hours
  • Research university
  • Research & Education
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Questions? Contact our recruiter

  • Closing date 30-01-2023
  • 52.06.22.TDA

Job description

In this project on End-to-end deep learning quantitative MR reconstruction we aim to substantially accelerate quantitative MRI acquisitions and their reconstruction. As a PhD student, you will develop and apply advanced MR image reconstruction techniques to efficiently obtain clinically useful images from short acquisitions. This is a challenging and multidisciplinary project, with the aim to translate and combine findings from fundamental research and advanced imaging techniques, into clinically applicable and highly relevant diagnostic tools.

This project is part of a new ambitious Innovation Center for Artificial Intelligence (ICAI lab) within the national ROBUST consortium that is funded by the Dutch Research Council and industry. ROBUST focuses primarily on the development of trustworthy AI technology for the resolution of socially relevant issues, such as those in healthcare, logistics, media, food and energy. Our ICAI lab Trustworthy AI for Magnetic Resonance Imaging, consists of 5 PhD projects that focus on the development and validation of novel AI assisted MRI acquisition and reconstruction methods, and the translation of these to clinic. There will be strong collaboration with the other PhD students in the ICAI lab.

Work environment

As PhD student you will be employed in the department of Radiology & Nuclear Medicine of the Erasmus MC, University Medical Center Rotterdam. The research will be conducted in collaboration with the Erasmus University Rotterdam and the industrial sponsor and collaborator GE Healthcare. The department of Radiology & Nuclear Medicine of the Erasmus MC is one of the largest imaging departments in Europe with 10 clinical MRI scanners at several field strengths and 1 PET-MRI scanner. The department has an international character with a good balance between internationally recognised, high-end research and an excellent social working environment. The PhD student will be embedded in the MR physics group and Biomedical Imaging Group Rotterdam (BIGR), which is a large group of researchers with technical background developing novel image analysis, reconstruction, and AI methods. Additionally, you will collaborate with scientists of GE Healthcare, with an opportunity to work at one of their research facilities for some time if and when appropriate. We do not discriminate on the basis of sex, gender, belief, culture, place of birth or occupational impairment when recruiting and selecting staff and students.

The supervision for the projects will be provided by Dr. Dirk H.J. Poot and Dr. Ir. Stefan Klein in collaboration with GE Healthcare.

Qualifications and skills

  • You should be an independent and highly motivated researcher with an MSc or MEng degree in a technical domain (e.g., Physics, Engineering, Mathematics, Computer Science, Biomedical Technology).
  • Affinity for or experience with machine learning, MR image acquisition, processing and/or programming is highly valued.
  • You should be able to communicate and work with clinicians as well as researchers from various fields

Before you apply please check our conditions for employment.

Terms of employment

As part of the collaboration in this project research visits to GE Healthcare (Munich, Germany) are envisioned.

  • You will receive a temporary position for 4 years. The gross monthly salary is € 2.789,- in the 1st year and increases to € 3.536,- in the 4th year (scale OIO).
  • A 4% Collective Labour Agreement payraise starting November 2023.
  • Excellent fringe benefits, such as a 13th month that is already paid out in November and an individual travel expense package.
  • Pension insurance with ABP, we take care of approximately 2/3 of the monthly contribution.
  • Special benefits, such as an in-company physiotherapist and bicycle repairer. There is also a gym where you can work on your fitness after work.

More information

For more information about this position, please contact Dr. ir. D.H.J.Poot, phone number: +31 10 7043442 or e-mail: d.poot@erasmusmc.nl.

No agencies please.

Application proces

Step 1 - Apply

Did we get you excited about this position? Submit your application through the application button. You will receive a confirmation of receipt from our recruiter right away.

Step 2 - Selection

Based on your application, we check to see if there is a fit between us. We will let you know as soon as possible whether you are invited for an interview.

Step 3 - Job interview

You have been invited for an interview, great! In this first meeting we get to know each other and see if You can form an idea of the position, the department and Erasmus MC. If the interview goes well usually a second interview follows.

Step 4 - Offer and terms of employment

It’s a match! Your future manager will discuss your salary and employment with you. You will also receive more information about our other terms of employment.

Step 5 - Getting started

Your first working day has come! We are more than happy to have you. Your new department will give you a warm welcome and provide you with all the information you need. Enjoy your job at Erasmus MC!

Contact me

Jerry Chandansingh
Recruiter

Jerry Chandansingh
Erasmus MC

Pioneering, pushing boundaries and leading: in healthcare, education and research

Erasmus MC is an international leading academic hospital in Rotterdam. We are recognized as a world-class scientific research organization aiming to improve our understanding of diseases and disorders and helps to predict, treat and prevent them. We have access to the latest equipment and techniques. A working environment that gets the best out of people.

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Apply for PhD candidate in the new ICAI lab Trustworthy AI for Magnetic Resonance Imaging: project on End-to-end deep learning quantitative MR reconstruction