Pica Code-099 Segmentation and multimodal analysis of 3D medical images through efficient deep learning architectures (Italia)

Pica Code-099 Segmentation and multimodal analysis of 3D medical images through efficient deep learning architectures (Italia)

10 nov
|
Italian Ministry of Education, University and Research
|
Italia

10 nov

Italian Ministry of Education, University and Research

Italia

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

Tutte le competenze, le qualifiche e l'esperienza necessarie per un candidato di successo sono elencate nella seguente descrizione.
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 xysqume 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

📌 Pica Code-099 Segmentation and multimodal analysis of 3D medical images through efficient deep learning architectures (Italia)
🏢 Italian Ministry of Education, University and Research
📍 Italia

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