3 open PhD positions in EU Doctoral Network
We are looking for PhD candidates for 3 open positions in the areas of machine learning, neuroimaging, neurooncology, and brain development in a starting EU project BRIDGE-AI. This project will connect 15 international PhD students in a EU wide doctoral network.
Please apply here: BRIDGE-AI Open Positions (This is the website of the coordinating university, through which applications from candidates will be centrally managed. Please apply for DC8, DC11, or DC12 for our PhD projects). The PhD students at CIR will be supervised by Roxane Licandro, and Georg Langs at CIR. PhD students will be members of the Comprehensive Center of AI in Medicine.
DC8: Linking Imaging Phenotypes and Epigenomics in Brain Tumors
This PhD project sits at the frontier of AI-driven cancer research, aiming to connect medical imaging with epigenomic and clinical data to understand and predict brain tumor progression and treatment response. You will develop novel machine-learning methods—including generative models and multi-agent systems—to uncover links between observable imaging patterns and the underlying tumor biology. Working closely with experts in machine learning, neuro-imaging, pathology, and oncology, you will help build predictive models that move beyond correlation toward mechanistic insight and patient-specific forecasting, with direct relevance for improving brain-tumor care.
DC12: Trajectory Learning of Fetal Brain Development for Early Detection of Malformations
This project focuses on modeling fetal brain development as a dynamic trajectory, with the goal of detecting early deviations that signal brain malformations. Using in-utero MRI data from mid-gestation to term, you will develop time-conditioned and manifold-learning approaches to capture normative brain surface development and cortical folding. By identifying where and how individual fetuses diverge from typical developmental paths, you will derive interpretable, explainable risk scores for malformation emergence—designed with clinical prenatal diagnosis in mind. The work combines advanced machine learning, neuroimaging, and explainable AI with real translational potential.
DC12: Paths of Functional Reorganization and Plasticity in Brain Tumor Patients
This PhD project explores how the human brain reorganizes in response to tumors, with a particular focus on language networks and higher cognitive functions. You will analyze anatomical and functional brain imaging data to model how cortical networks adapt, compensate, or fail—and what this reveals about brain plasticity, evolution, and cognitive resilience. By developing computational and embedding-based methods that disentangle anatomy from functional interactions, you will map cortical regions capable of reorganization and identify mechanisms driving functional recovery or decline. The project combines neuroscience, machine learning, and cancer research, with clear implications for therapy planning and outcome prediction in neuro-oncology.