To optimise the implementation of DL models in the clinical radiotherapy workflow, we are developing an automated quality assurance (auto-QA) system that can automatically detect when the model makes a mistake and what the consequences of these mistakes are for the patient’s treatment plan. One of the elements of this system will be to detect when a DL segmentation model is applied to data outside its training distribution.
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You will conduct an overview of recent literature on promising out-of-distribution detection methods
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You will implement, analyse, and compare different methodologies to detect out-of-distribution inputs to a DL segmentation model (e.g. generative AI, uncertainty quantification, model feature distance, etc.)
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You will collaborate with the research team on how to integrate this element in the greater auto-QA tool
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You will contribute to the usability of AI by improving the implementation of AI in a clinical workflow
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There is room for your own input
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There is interest to turn the results into an academic publication
For this project, we are looking for a university master’s or bachelor’s student with, for example, a background in artificial intelligence, computing science, applied mathematics, or a similar field.
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You have an interest in clinically applicable research
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You have experience with ML/DL modelling
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You are able to independently conduct research and have strong writing skills
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You enjoy collaborating with team members to develop innovative solutions
- Internship agreement with UMCG
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Good supervision at UMCG
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Scientific working environment (AI in radiotherapy group)
Neem voor meer informatie contact op met:
[email protected]
Good to know: in consultation, you can partly work from home.
Interested?
Feel free to take some time to consider this vacancy, but don’t wait too long… We will close the vacancy once we find a suitable candidate (the closing date is fictitious).
You can easily apply via the application button.
After receiving your application, you will immediately receive a confirmation. We select once a week and invite suitable candidates for an interview. Is there a match? Then we will register you for the UMCG internship agreement.
