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

Italian Ministry of Education, University and Research; Italia; On-site; EUR 32,000 - 45,000; Full time

Job summary

Unimore in Italy seeks a researcher to advance deep learning methods for segmentation and analysis of 3D medical volumes, including Cone-Beam CT and brain MRI. The appointment targets candidates with strong neural-network skills and medical imaging experience.

The role involves designing, training and evaluating architectures (CNNs, Transformers, State Space Models) with emphasis on scalability on HPC/GPU environments and collaboration with imaging teams.

Qualifications

• Experience in deep learning for 3D medical image analysis.
• Familiarity with CBCT and MRI modalities.
• Proven ability to design and evaluate neural architectures.

Responsibilities

• Develop deep learning methods for segmentation of 3D medical volumes.
• Evaluate architectures (CNNs, Transformers, State Space Models) for accuracy and efficiency.
• Implement multimodal fusion and image synthesis techniques.

Skills

Deep learning

3D medical image segmentation

Convolutional neural networks

Transformer architectures

State Space Models

High-performance computing

Tools

Mamba

GPU HPC

Job description

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