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Perform experiments to test the effect of alternating gravity (clinostat) on bacterial growth.
The goal of this internship is to extend automatic algorithm selection to different image processing areas for Radiomics.
The goal of this internship is to find a deep learning segmentation approach to create robust medical image features.
In this project, you will develop improved image analysis methods for automated diagnosis of Alzheimer’s disease.
In this project, you will develop a method to accurately assess atrophy in longitudinal data to study the progression of Alzheimer’s disease.
In this project, you will work on the quantification of cerebral blood flow from ASL and apply the event-based model to study the processes of aging and disease.
Research with impact @ TU Delft Industrial Design Engineering
Radiotherapy is conventionally planned on CT images. Imaging provided at the treatment site often is Cone Beam CT, which can not be directly used to create (or update) radiotherapy plans. Development of a method that automatically transforms such a Cone Beam CT to a normal CT may facilitate faster and more accurate treatment.
We are looking for a motivated MSc student to create a microfluidics platform in Eindhoven which they then would combine with a 3D cell culture model of sarcoma developed in Leiden. Microfluidics will be used to enhance the representability of the model in prediction of patient treatment response. The MSc student would be jointly supervised by Prof. Jaap den Toonder (Microsystems group, TU/e) and Prof. Judith Bovee (department of Pathology, LUMC), both experts in their fields. Some travel would be involved. Initially most of the work would entail design and production of the microfluidic chip in Eindhoven and later culture of sarcoma within the chip in Leiden.
Work at the junction of technical (TU Delft) and clinical (LUMC) research.