How to apply?

How to apply:
If you are interested, please send the following information to aiforhealth@radboudumc.nl:

  • Motivation letter (max 1 A4).
  • CV (max 1 A4).
  • Support letter head of department (see format here). → A letter from your head of department or supervisor about their support and permission to let you follow the course.
  • The track you would like to follow (short or in-depth track, see course program for details).

More information and instructions how to apply can be found on this internal Radboudumc page.


Testimonials


"The AI4health course was a great success. It provides a thorough introduction to machine learning, but perhaps even more instructive is that you learn how to use machine learning to bring about changes in practice. In that respect, you learn that machine learning is relatively simple and data science — extracting value for practice from your data — (more) difficult.

All in all, as a medical specialist, deputy educator and post-doc researcher, it has given me the opportunity to add new methodology to my research arsenal, to have a better understanding of where opportunities lie for data science using machine learning ( and where not and/or what is needed) and it made me think again from a different perspective about our profession. I have currently written two grant applications that include machine learning and have already had great discussions with colleagues about its application.

Moreover, it was also a really fun course, because you work on projects with colleagues from all over the hospital. And important to mention: a solid background in statistics makes it easier, but is really not necessary. You can focus just as well on valorizing the results of the analyzes as on analyzing the data." according to Dirk Geurts, medical specialist, deputy educator and post-doc researcher, Department of Psychiatry at the Radboudumc.


"Whow this course really broadened my knowledge into the world of artificial intelligence (AI). It brought me a very good overview on the wide applicability of AI in healthcare, and I also was able to gain adequate experience in Python programming which I was completely lagging.

The course covers a wide variety of topics (like data integrity, data visualization, data engineering, text mining, decision support, machine learning, deep learning) given by expert scientist, after having a thorough introduction into the field of statistical learning, data mining business model understanding, and Python programming given by experts from the Jheronimus Academy of Data Science (JADS).

The scientific days were alternated by work in project groups, for which one could applicate your own research proposals (bringing your own data). I was privileged to work on my proposal, which really boosted my next step into the scientific research in deep learning on ultrasound images. During these project days one is able to work on skills of your own ambition.

All course- and project days were very interactive and had a powerful combination of theoretical lectures as well as practical assignments and excellent supervision, which made it challenging and fun but above all educational.

I would not have wanted to miss this course and can recommend it to everyone who would like to get familiar with the world of AI or like to master/obtain Python skills!" according to Gert Weijers, researcher at the Medical UltraSound Imaging Center (MUSIC), Department of Medical Imaging, Radboudumc.