PhD Position in Deep Learning Based 3D X-Ray Tomography for Cargo Inspection

Department: Department of Physics

Regime Full-time

PhD student position in Deep learning based 3D X-ray tomography for cargo inspection (f/m)

Description:

This PhD position is part of a large European project on cargo inspection:

Within the field of security, Customs and Border inspection have not had breakthrough technological developments in the last 20 years, since the introduction of X-ray screening. The limits of these current technologies are accentuated by the increasing diversity and novelty in trafficking materials, tools and methods. These limitations combined with the growing needs of inspection and control call for a disruptive innovative solution. Wanting to move a step up from the existing planar scanning methods with limited material identification results, several studies have identified potential solutions focused on: - High energy 3D X-ray tomography - Neutron interrogation/photofission - Nuclear resonance fluorescence (NRR) While these show good results and performances, they also have several important drawbacks, which limits their possible uses. Moreover, these solutions do not have common technological bricks meaning they can only lead to separate disposals. The proposed MULTISCAN3D investigates a new all-in-one system whose purpose is to become simultaneously a userfriendly, flexible, relocatable solution offering high-quality information for: - Fast high energy 3D X-rays tomography (as first line) - Neutron interrogation/photofission (as second line) - Narrow gamma ray beam based NRR (as second line)

MULTISCAN3D will start by investigating and defining needs and requirements, in a technologically-neutral way, with Europe’s most prominent Customs Authorities which will be translated to technical specifications. The main body of the research will be focused on three parts, following which, lab validations and real-environment demonstration will be carried out. These three work areas are: - Laser-plasma based accelerators as X-ray sources - 3D reconstruction for multi-view configurations and data processing - Detectors and source monitoring At the same time complementary techniques with chemical and SNM identification capabilities will be investigated.

In your PhD research, you will develop deep learning based reconstruction methods for sparse view X-ray imaging with application to cargo inspection.

Qualifications:

• You have a M.Sc. degree in Mathematics, Physics, or Engineering.
• You love interdisciplinary science, and want to advance the field of tomography with your skills, creativity and novel solutions.
• You like to understand and develop theory so you can put your new methods into practice.
• You also want to tell the world about your findings with sparkling presentations.
• Knowledge about tomography is not required, but certainly a plus.

Imec-Visionlab:

The Vision Lab (http://visielab.uantwerpen.be/) is an imec research group of the physics department at the University of Antwerp. The lab has unique expertise in the development of algorithms for reconstruction, processing and analysis of tomographic imaging data. The working environment is strongly interdisciplinary, combining techniques and insights from Physics, Mathematics and Computer Science. The group has a broad range of national and international collaborations with both academic and industrial partners.

Our offer:

• An exciting research trajectory towards a PhD in X-ray tomography.
• Multidisciplinary cooperation with strong academic research and industrial partners.
• A world-class research environment with state-of-the-art X-ray and computer infrastructure and an exciting collaboration with European partners.

Starting date:  July 1, 2021.

Want to apply?

• Please apply through the University of Antwerp’s online job application platform. Click on 'apply', complete the online application form and don’t forget to include the following document(s): 1) a motivation letter, 2) a detailed CV (including followed courses, honours, grades, previous work, programming skills, publications, …) 3) contact info of two references 4) a weblink to a short video introducing yourself
• The selection committee will review all of the applications on a running base. As soon as a decision has been made, we will inform you about the next steps in the selection procedure.
• If you have any questions about the online application form, please check the frequently asked questions or send an email to jobs@uantwerpen.be. If you have any questions about the job itself, please contact jan.sijbers@uantwerpen.be or jan.debeenhouwer@uantwerpen.be