PhD Student-ESR1

MSC-ITN (EU2020) RAPTOR project: ESR1 - Dose accumulation and uncertainty estimation

Your tasks

The RAPTOR (Real-time Adaptive Particle Therapy Of Cancer) Maria Sklodowska-Curie Innovative Training Network (ITN) is recruiting 15 highly motivated PhD students: two of them will be enrolled at PSI. The offered positions are available with a starting date from summer 2021. More information on the available positions and on the selection criteria are available on the RAPTOR web-page:

Background: Proton therapy PT is an advanced type of radiotherapy used to treat a constantly rising number of cancer patients. PT allows to target tumour with a high accuracy while sparing healthy surrounding tissue from dose. However, changes in anatomy or positioning and organ motion give rise to uncertainties which need to be further minimized to exploit the full benefits of PT. Adapting PT plans in real time has the potential to provide truly personalized treatments, allowing for better target control and less toxicity. The main objective of RAPTOR is to advance the proton therapy for real-time use.
The researchers recruited in RAPTOR will conduct research projects at both academic and non-academic health care facilities which will sharpen your focus on clinical needs with respect to real-time adaptive PT. The active involvement of industry ensures that the transfer of industry-relevant skills is an integral part of individual projects. The recruited candidates will acquire a broad range of advanced and transferable skills within a unique, innovative, multidisciplinary and inter-sectoral training environment. Regular training schools and secondments to other EU academic and industrial partners are planned for each project.

As part of the adaptive process, new volumetric images of the patient are acquired every day. In order to accurately record the treatment dose delivered to the patient, it is necessary to accumulate the dose distribution optimized on the daily image on a common anatomy. For this process, a deformable image registration (DIR) between each daily volumetric image and the reference image has to be performed. It is well known that this task is ambiguous, in that there are many different solutions to the DIR problem between any two data sets, none of which is necessarily correct. Consequently, the dose accumulation is also inherently uncertain.

The main goal of the PhD project is to recognise regions and treatment plans with a reduced sensitivity to DIR uncertainties and with an increased robustness of the accumulated dose. The candidate will address this (i) by improving the quality of the DIR algorithm and (ii) by developing a visualization tool to guide the user to the selection of the treatment plan more robust to (i.e. less affected by) DIR uncertainties.

Your profile

  • MSc degree in Physics, Biomedical Engineer, Software Engineer or related studies
  • Have less than 4 years of research experience (after obtaining the master degree)
  • Have to comply to the Europea mobility rule (applicants have not resided or carried out her/his main activity in the country to be recruited in for more than 12 months in the last 3 years)
  • Fluent in English (oral and written)
  • Research interest and ambitions for excellence in medical physics
  • Analytical skills and ability to work independently
  • Good communication skills relevant for working in an international and interdisciplinary research group
  • Background in proton therapy, scientific computing, image processing, machine learning is an asset

We offer

Our institution is based on an interdisciplinary, innovative and dynamic collaboration. You will profit from a systematic training on the job, in addition to personal development possibilities and our pronounced vocational training culture. If you wish to optimally combine work and family life or other personal interests, we are able to support you with our modern employment conditions and the on-site infrastructure.

For further information and to apply please visit
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For further information, please contact Dr Francesca Albertini, phone +41 56 310 52 39 or e-mail

Please submit your application online by 15 March 2021 (including addresses of referees) for the position as a PhD Student-ESR1 (index no. 1720-00).

Paul Scherrer Institut, Human Resources Management, Patrizia Meister, 5232 Villigen PSI, Switzerland
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