Summary
An exciting opportunity is available for a Postdoctoral Researcher to join the groundbreaking ERC Synergy Project Zee-Zoom-Zap, focused on developing a new paradigm for cancer theranostics—integrating diagnosis and treatment through advanced artificial intelligence and physics-based methodologies. The position is hosted at Universitat Pompeu Fabra (UPF), Barcelona, Spain, and offers the chance to contribute to cutting-edge research at the intersection of deep learning, mathematical modeling, medical imaging, and healthcare technology.
Postdoctoral Researcher – Physics-Informed Deep Learning for Cancer Imaging, Diagnosis and Treatment (ERC Synergy Project Zee-Zoom-Zap)
Summary Table
| Category | Details |
|---|---|
| Position Title | Postdoctoral Researcher – Physics-Informed Deep Learning for Cancer Imaging, Diagnosis and Treatment |
| Project | ERC Synergy Project Zee-Zoom-Zap (2026–2032) |
| Institution | Universitat Pompeu Fabra (UPF) |
| Department | Department of Engineering |
| Research Unit | BCN Medtech |
| Location | Barcelona, Spain |
| Position Type | Open-ended Senior Postdoctoral Position |
| Supervisor | Prof. Miguel Angel Gonzalez Ballester (ICREA Professor) |
| Research Focus | Physics-Informed Deep Learning, Medical Imaging, Cancer Theranostics |
| Required Qualification | PhD in Engineering, Computing, Physics, Mathematics, or Related Fields |
| Working Language | English |
| Start Date | Immediate (Flexible Start Available) |
| Application Deadline | 30 June 2026 |
| Application Method | Email Application |
Designation
Postdoctoral Researcher – Physics-Informed Deep Learning for Cancer Imaging, Diagnosis and Treatment
Research Area
The successful candidate will conduct research in:
- Physics-Informed Deep Learning
- Neural Operators and Scientific Machine Learning
- Medical Image Analysis
- Cancer Imaging and Theranostics
- Image Processing for Healthcare
- Computational Simulation
- Biomedical Engineering
- AI-Assisted Diagnosis and Treatment
- Medical Device Design and Therapy Guidance
Location
Department of Engineering
Universitat Pompeu Fabra (UPF)
Barcelona, Spain
The research will be carried out within BCN Medtech, a leading research unit specializing in biomedical engineering, medical image analysis, and computer-assisted surgical planning, simulation, and navigation.
Eligibility / Qualification
Applicants must possess:
- A PhD degree in Engineering, Computing, Physics, Mathematics, or a closely related discipline.
- Strong research background in machine learning and computational methods.
- Excellent communication skills in English.
Preferred qualifications include:
- Experience in medical image processing and analysis.
- Expertise in computational simulation for healthcare applications.
- Knowledge of physics-informed deep learning frameworks.
- Experience with neural operators and scientific machine learning methods.
- Demonstrated ability to conduct independent and collaborative research.
Job Description
The selected postdoctoral researcher will play a leading role in exploring innovative physics-informed formulations for deep learning within the ERC Synergy Zee-Zoom-Zap project.
Key responsibilities include:
- Developing novel methodologies that combine modern AI with classical mathematical and physical models.
- Advancing research in physics-informed deep learning and neural operators.
- Designing and implementing machine learning algorithms for cancer imaging and treatment applications.
- Contributing to image processing, imaging systems, and therapy-guidance technologies.
- Collaborating with multidisciplinary researchers across engineering, mathematics, physics, and healthcare.
- Publishing research findings in leading scientific journals and conferences.
- Supporting the overall scientific objectives of the Zee-Zoom-Zap project.
How to Apply
Interested candidates should submit:
- Updated Curriculum Vitae (CV)
- Any additional supporting documents relevant to their application
Applications should be sent via email to:
Prof. Miguel Angel Gonzalez Ballester
Email: ma.gonzalez@upf.edu
Applications will be reviewed on a rolling basis. Early applications are strongly encouraged as the position may be filled before the final deadline.
Last Date to Apply
30 June 2026
Note: Applications will be considered on a rolling basis, and the position may close before the deadline if a suitable candidate is identified.
