Post-doctoral fellow in advanced AI-assisted 3D and 4D X-ray imaging for medical and materials science applications (PA2024/1978)

The Division of Synchrotron Radiation Research (www.sljus.lu.se ) is a part of the Department of Physics and has more than 40 employees. The focus of the research is on experimental studies of electronic, structural, and chemical properties of materials. The Division is developing a new activity focussing on magnetic properties. At the Division we use and develop a wide range of large facility- and lab-based techniques. We are additionally engaged in the development of the MAX IV Laboratory in the fields of beamlines, experimental stations, techniques for Synchrotron radiation and the accelerator systems. MAX IV (http://www.maxiv.lu.se/ ) is a national large-scale research laboratory hosted by Lund University and situated within biking distance from the Department of Physics. It provides scientists from Sweden and the rest of the world with the most brilliant Synchrotron radiation source and instrumentation for research in areas such as materials science, structural biology, chemistry and nanotechnology.

The Department of Medical Radiation Physics (www.msf.lu.se ) is interdisciplinary, and has research divisions covering all aspects of medical physics, such as x-ray imaging, radiation therapy, mathematics and physics of medical imaging, nuclear medicine and magnetic resonance. Environmental radiology, biological effects of radiation and radiation protection for ionizing and non-ionizing radiation are other important fields of scientific interest. For more information about the main supervisors of the project, please see https://portal.research.lu.se/en/persons/martin-bech and https://portal.research.lu.se/en/persons/pablo-villanueva-perez .

X-rays have a unique ability to investigate samples in their natural state, such as cells, proteins in solution or electronic devices, potentially at resolutions up to the atomic level. The advent of diffraction-limited storage rings (MAX IV) and X-ray free-electron lasers (European XFEL) has opened a whole new spectrum of applications due to their unprecedented brilliance. This project aims to enable new spatiotemporal frontiers while reducing the dose using state-of-the-art X-ray sources.

In particular, the candidate will work on the development of novel deep-learning reconstruction algorithms to retrieve 3D and 4D (3D+time) imaging acquired by advanced X-ray imaging techniques. Such developments imply merging state-of-the-art deep-learning approaches with physical knowledge of the studied processes and X-ray imaging formation.

The developed algorithms will be tested and commissioned in different synchrotron radiation facilities: Primarily at MAX IV (Sweden), but also at ESRF (France), SLS (Switzerland), PETRA III (Germany), and the European XFEL in Germany.

The project is computational. The successful candidate will have the opportunity to:

• Establish new deep-learning-based algorithms to reconstruct images in 3D and 4D
• Develop new image reconstruction algorithms for 3D and 4D X-ray data analysis, merging physical X-ray knowledge and deep-learning approaches.
• Collaboration with clinitians, industry, and wider society.
• Contribute to one of the biggest challenges of medical X-ray imaging, i.e., dose reduction.

The work will be performed in collaboration with several other researchers at local and international facilities, including Lund University Hospital, ETHZ (Switzerland) and Paul Scherrer Institute (Switzerland). Furthermore, the candidate is expected to interact with PhD students and master’s students within the Division and the involved research groups.

The candidate will have significant freedom and is expected to take initiative and drive the project quite independently.

The main duties involved in a post-doctoral posistion is to conduct research. Teaching may also be included, but up to no more than 20% of working hours. The position shall include the opportunity for three weeks of training in higher education teaching and learning.

Appointment to a post-doctoral position requires that the applicant has a PhD, or an international degree deemed equivalent to a PhD, within the subject of the position, completed no more than three years before the date of employment decision. Under special circumstances, the doctoral degree can have been completed earlier.

• Very good oral and written proficiency in English.
• The subject of the doctoral degree should be Physics, Computer Science, or equivalent.

This is a career development position primarily focused on research. The position is intended as an initial step in a career, and the assessment of the applicants will primarily be based on their research qualifications and potential as researchers.

Particular emphasis will be placed on research skills within the subject.

• A good ability to develop and conduct high quality research.
• Teaching skills.

• Experience in machine learning
• Experience in image reconstruction, specifically in 3D and 4D.
• Experience in imaging
• Experience in Big Data
• Experience in X-ray imaging and X-ray experiments with a focus either in materials science (e.g., additive manufacturing) or medical applications
• Experience with programming and using version control software

Consideration will also be given to good collaborative skills, drive and independence, and how the applicant’s experience and skills complement and strengthen ongoing research within the department, and how they stand to contribute to its future development.

This is a full-time, fixed-term employment of a maximum of 2 years. The period of employment is determined in accordance with the agreement “Avtal om tidsbegränsad anställning som postdoktor” (“Agreement on fixed-term employment as a post-doctoral fellow”) between Lund University, SACO-S and OFR/S, dated 1st February 2022.

Applications shall be written in English and be compiled into a PDF-file containing:

• résumé/CV, including a list of publications,
• a general description of past research and future research interests (no more than three pages),
• contact information of at least two references,
• copy of the doctoral degree certificate, and other certificates/grades that you wish to be considered.