Postdoc: MRI Pancreatic Cancer Imaging & Deep Learning

European Society for Magnetic Resonance in Medicine and Biology; Paris; On-site; EUR 36,000 - 47,000; Full time

Benefits offered by this job

Dynamic environment

State-of-the-art imaging facilities

Professional development support

Job summary

European Society for Magnetic Resonance in Medicine and Biology seeks a Postdoctoral Researcher to advance MR imaging of pancreatic cancer. The role focuses on time-resolved diffusion and MR elastography for treatment response prediction and stratification, with access to clinical workflows and preclinical models.

You will train deep learning models for tumor segmentation, publish findings, and contribute to interdisciplinary imaging projects in Paris.

Qualifications

• PhD in biomedical engineering, physics, computer science or a medical degree.
• Programming proficiency in Matlab, Python or C/C++.
• Experience with MRI acquisition protocol development in preclinical or clinical settings.
• Basic knowledge of radiology and physiopathology of pancreatic cancer.

Responsibilities

• Conduct research projects on MR imaging for treatment response prediction in pancreatic cancer.
• Develop time-resolved diffusion imaging concepts and analyze diffusion coefficients.
• Apply MR elastography techniques with multiple acoustic sources and frequencies.
• Work with preclinical models and clinical workflows for pancreatic cancer.
• Train and optimize deep learning models for tumor segmentation and physics-informed tasks.
• Publish findings in peer-reviewed journals and conferences.

Skills

Matlab

Python

C/C++

MRI protocol development

Radiology knowledge

Education

PhD in biomedical engineering, physics, computer science, medical degree

Job description

European Society for Magnetic Resonance in Medicine and Biology seeks a Postdoctoral Researcher to advance MR imaging of pancreatic cancer. The role focuses on time-resolved diffusion and MR elastography for treatment response prediction and stratification, with access to clinical workflows and preclinical models.

You will train deep learning models for tumor segmentation, publish findings, and contribute to interdisciplinary imaging projects in Paris.