Pica Code-099 Segmentation and multimodal analysis of 3D medical images through efficient deep learning architectures
Organisation/Company Unimore Research Field Engineering » Computer engineering Researcher Profile Recognised Researcher (R2) Leading Researcher (R4) First Stage Researcher (R1) Established Researcher (R3) Application Deadline 18 Nov 2026 - 13:00 (UTC) Country Italy Type of Contract To be defined Job Status Not Applicable Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No
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)
- Italy
Eligibility of fellows: country/ies of residence:
- OTHER
Eligibility of fellows: nationality/ies:
- OTHER
Selection process
selective public procedure based on qualifications
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)
- Italy
Eligibility of fellows: country/ies of residence:
- OTHER
Eligibility of fellows: nationality/ies:
- OTHER
Selection process
selective public procedure based on qualifications