Pica Code IR-2026-099 Segmentation and multimodal analysis of 3D medical images through efficient deep learning architectures

id="offer-description" class="ecl-u-type-heading-2 ecl-u-mt-none ecl-u-mb-l">Offer Description Research activities: The appointment concerns the development of deep learning methods for the segmentation and analysis of three-dimensional medical volumes, with particular reference to Cone-Beam CT (CBCT) images of the maxillofacial region and brain magnetic resonance imaging (MRI). Activities will include: - design, training and evaluation of architectures for multi-structure 3D segmentation, including convolutional, Transformer and State Space Model (e.g., Mamba) architectures, with attention to computational efficiency and scalability on HPC/GPU infrastructures; - development of multimodal fusion strategies and methods to handle missing modalities (e.g., incomplete MRI sequences), as well as image synthesis methods;(omissis). Where to apply Website https://www.unimore.it/it . Requirements. Additional Information . Eligibility criteria. Eligible destination country/ies for fellows: . Italy. Eligibility of fellows: country/ies of residence: . OTHER. Eligibility of fellows: nationality/ies: . OTHER. Selection process. selective public procedure based on qualifications. Additional comments. Contact email: [email protected] . Contact phone: 059/2056447-6075-7078. Annual budget: 41.580. Frequency of calls: 12. International mobility required: no. Website for additional job details https://titulus-unimore.cineca.it/albo/. <h2