Overview
The Center for AI in Radiation Oncology (CAIRO) invites two PhD candidates to contribute to pediatric digital oncology. As a newly established research group within the Department of Radiation Oncology at Inselspital, CAIRO focuses on data‑driven and mechanistic models to improve diagnosis, treatment planning, and outcome prediction in radiation oncology. Supported by a Swiss National Science Foundation Starting Grant, CAIRO investigates how artificial intelligence can support precision therapy in pediatric oncology, with a particular emphasis on Diffuse Midline Glioma (DMG).
Project 1: Predictive Modeling for Biomarker Identification and In‑Silico Therapy Design in Pediatric Diffuse Midline Glioma
This PhD project develops AI‑based prediction models that link molecular tumor profiles to drug response. The goal is to establish a clinically relevant framework for predicting treatment response and for in‑silico identification of combination therapies in pediatric DMG. The project compares foundation models and mechanistic learning to guide model training regimes, feature selection, and architecture design based on the drug’s mechanism of action.
* Multi‑omics data integration from pediatric and pan‑cancer datasets, including public and private data provided by collaborators.
* Deep learning and explainable AI approaches for drug response prediction.
* Benchmarking foundation models versus mechanistic learning approaches for drug response modelling.
* Model interpretation to identify molecular determinants of treatment sensitivity and resistance, and to recommend combination therapies.
Project 2: Personalized Radiotherapy for Pediatric Diffuse Midline Glioma
This project develops computational models that predict individual radiotherapy (RT) response based on pre‑treatment imaging data and explores personalized adaptations of fractionation and dose distribution. The project combines mechanistic modelling with AI‑based image analysis to contribute to a framework for individualized RT planning in pediatric brain tumours.
* MRI‑based prediction of RT response using MRI foundation models.
* Mechanistic learning frameworks for tumour growth and radiosensitivity modelling.
* Counterfactual simulations of alternative fractionation schemes.
* Generative space‑time modelling for anatomical prediction of tumour recurrence for a given RT plan.
Candidate Requirements
* Master’s degree in a relevant field (e.g. computational biology, computer science, data science, physics, biomedical engineering).
* Solid programming skills in Python; experience with machine learning frameworks (PyTorch, TensorFlow, MONAI).
* Motivation to work in a collaborative, interdisciplinary environment involving clinicians and data scientists.
* Good communication skills and proficiency in English.
* Background requirements for Project 1: MSc in computer science, data science, bioinformatics, or a related field; experience in computational biology and the processing of omics data; interest in translational applications of AI in pediatric oncology.
* Background requirements for Project 2: MSc in medical physics, biomedical engineering, computer vision, or a related discipline; experience with medical image analysis and/or generative modelling; interest in radiotherapy modelling and quantitative imaging.
Benefits
* A fully funded 3.5‑year (Project 1) and 4‑year (Project 2) PhD position within the Faculty of Medicine and the Department of Digital Medicine at the University of Bern.
* Integration in a multidisciplinary team at the interface of medicine, physics, and computer science.
* Access to unique pediatric datasets through international collaborations.
* Support for conference participation and teaching/mentoring opportunities.
* High‑performance computing and clinical data environments within Inselspital and the University of Bern.
Application Instructions
* Curriculum Vitae.
* Cover letter (1–2 pages) outlining motivation and preferred project.
* Academic transcripts (Bachelor’s and Master’s).
* Contact information for two references or recommendation letters.
Application Deadline
30 November 2025 (applications will be reviewed on a rolling basis until the positions are filled).
Please send your application to sarah.brueningk@unibe.ch with the subject line “PhD Application – Pediatric Digital Oncology”.
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