Positron Emission Tomography (PET) is vital for diagnosing diseases like cancer, heart conditions, and neurological disorders. However, conventional PET/CT imaging exposes patients to radiation from tracers and CT scans. This Swiss National Science Foundation (SNSF)-funded project aims to address these challenges by creating data-driven models to enable high-quality, low-dose PET imaging, reducing radiation risks and enhancing accessibility without compromising diagnostic accuracy. This position focus on three main streams:
1. Denoising of Low-Dose PET Images: Developing models to reduce noise and enhance the quality of PET scans acquired at lower radiation doses.
2. CT-Free Attenuation Correction: Developing AI-driven techniques for PET imaging that eliminate the need for CT scans, reducing radiation exposure and associated costs.
3. Robust Image Reconstruction and Synthesis: Using deep learning to create high-quality PET and CT images from low-dose PET data to support accurate, non-invasive diagnostics.
This work aims to advance personalized, non-invasive diagnostic techniques, making PET imaging safer and more accessible in clinical practice.

We are seeking a highly motivated PhD candidate to join our team in Switzerland, bringing a strong background in deep learning and a passion for medical imaging. The PhD candidate will play a central role in developing robust and generalizable AI methods to address key technical challenges in PET image reconstruction.

Key responsibilities include:
• Design and implement advanced denoising models for low-dose PET images, focusing on noise reduction while preserving diagnostic detail.
• Develop and validate CT-free attenuation correction techniques that enhance PET imaging without additional radiation exposure.
• Collaborate closely with interdisciplinary teams at the University of Cambridge, Tsinghua University and Luzerner Kantonsspital to integrate AI solutions into clinical practice.
• Contribute to top scientific publications and outreach activities.
The project offers access to high-quality medical imaging datasets, a collaborative research environment, and guidance from academic and clinical experts in medical imaging and AI. The selected candidate will be employed for 48 months on the project.

• Master’s degree in Computer Science, Biomedical Engineering, or a related discipline.
• Strong background in deep learning with practical experience in medical imaging applications preferred.
• Proficiency in Python and machine learning frameworks such as PyTorch or TensorFlow.
• Excellent communication skills in English and an ability to work within international, multidisciplinary teams.
• An eagerness to contribute to impactful research in the field of medical diagnostics.

• Fully funded by the Swiss National Science Foundation (SNSF)
• The opportunity to work within an international research network and to contribute to the development of life-saving imaging technologies.
• Access to state-of-the-art datasets, resources, and facilities, fostering your growth in a supportive and collaborative environment.

Please submit your CV, a cover letter detailing your research experience and motivation, and contact information for two academic references. Applications will be reviewed on a rolling basis until the position is filled.

For inquiries or to submit your application, please contact: Angelica Aviles-Rivero (aviles-rivero@tsinghua.edu.cn) and Javier Montoya (javier.montoya@hslu.ch)