Call for applications for a fully financed PhD fellowship
Glioblastoma is the most common primary brain cancer. Partial brain radiotherapy is administered after surgery and is a key element in the treatment. The radiation target volume is outlined on MRI-scan and similarly defined for all patients: the gross tumour volume is based on the surgical cavity and residual visible tumour. To account for potential invisible tumour infiltration into normal brain, the gross tumor volume is expanded with 1.5 cm in all directions to form the clinical target volume, which is treated with a homogeneous radiation dose. So, all patients are treatet the same, but extensive research shows large variation in the distance and direction of invisible tumour infiltration across patients. With this project we aim to personalise the target to match the individual patient’s risk of tumour infiltration. Personalisation is expected to be an effective method to reduce treatment toxicity, while improving disease control.
We have prepared a dataset of 760 newly diagnosed glioblastoma patients treated in Denmark between 2014-2019. This data includes segmented longitudinal MRI-scans, and is worldwide unique. In this PhD project, you will utilize this dataset to develop new approaches for more personalized treatment, ranging from subgroup identification to fully individualized patient specific margins. To achieve this, you will combine statistical methods with image-processing methods, including machine-learning. This PhD project has high potential to shape the way glioblastoma patients will be treated in the future, within and beyond the borders of Denmark.
This PhD project is fully funded by the Danish Cancer Society, and affiliated with the Danish Comprehensive Cancer Center-Brain Tumor Center. The main workplace will be at the Danish Center for Particle Therapy (DCPT), Aarhus University Hopital. Associate professor Jasper Nijkamp will be the main supervisor from the Dept. of Clinical Medicine, providing expertise in image-processing and machine learning. You will work closely together with Radiation Oncologist Anouk Trip (Medical Doctor, PhD) for supervision on the clinical content. Furthermore, math professor Asger Hobolth is involved for data science supervision. At the DCPT you will be part of a large research community, with several research groups active in the field of radiation oncology
We are looking for a candidate who has, or will soon obtain, a master degree in biomedical engineering, computer science, data science, statistics, medicine, or similar.
Programming experience in Python, R, Matlab, or similar is a must.
Excellent verbal and written communication skills in English will be an advantage.
Experience with medical data research, image-processing and/or machine learning will be an advantage.
Please submit your application via this link. Application deadline is 19 January 2025 23:59 CET. Preferred starting date is 1 March 2025.
For information about application requirements and mandatory attachments, please see our application guide.
Please contact Associate Professor Jasper Albertus Nijkamp, jaspernijkamp@clin.au.dk for more information.
All interested candidates are encouraged to apply, regardless of their personal background. Salary and terms of employment are in accordance with applicable collective agreement.
