PhD candidate 'brainXplain'
Tuesday 7th September 2021
Organization: UMC Utrecht
Location: the Netherlands
Title: PhD candidate 'brainXplain'
Email Address: firstname.lastname@example.org
Description: Machine learning and artificial intelligence (AI) techniques are often considered ‘black box’. It can be difficult to understand their internal workings, which affects how medical decisions based on such algorithms are received. Modern technology, also known as "explainable AI”, can be used to gain insights into the decision making of AI. In SVD, it can be used to assess multiple lesion types, have multi-variate output, and simultaneously explain which lesion locations are most strongly associated with the output.
Your role in this project will be to develop and evaluate explainable AI technology. You will implement a number of explainable AI techniques that pinpoint which lesion locations are most strongly associated with the output (e.g. causative factors or clinical outcomes). Next, these methods will be validated on international datasets of thousands of patients of the Meta-VCI-Map consortium. Finally, a number of pilot studies will demonstrate potential applications assessing the relation between burden/distribution of SVD lesions and aetiology, cognitive outcomes, and prognosis.
You will be working at the internationally renowned Image Sciences Institute at the UMC Utrecht, which is housed within the Imaging Division. You will work in a team of PhD candidates and post-docs in the field of medical image analysis and machine learning. In this interdisciplinary project, there will be close collaborations with the Departments of Radiology and Neurology of the UMC Utrecht; and international partners of the Meta-VCI-Map consortium.
You are an excellent candidate with an MSc in computer sciences, medical imaging, physics, mathematics, biomedical engineering or a similar field. You have a strong interest in medical image analysis and machine learning. You have a good scientific background, are highly motivated and independent, able to work in an interdisciplinary team of engineers and medical doctors. Programming experience is required and knowledge of machine learning is an advantage.
Closing Date: 30 September 2021