PhD Position - Image based prediction of mosquito bed net conditions

Updated: 5 months ago
Job Type: FullTime
Deadline: 30 Sep 2022

The Department of Biomedical Engineering contributes to a better future in meeting health care needs by innovative biomedical research, and by translating basic science and engineering into medical knowledge and healthcare modernization. Our highly interdisciplinary department, consisting of 130 members, is a joint venture of the University of Basel, the University Hospital Basel and the University Children's Hospital Basel and is associated with researchers of the local life-science industry.

Together with the Swiss Tropical and Public Health Institute (SwissTPH), Professor Philippe Cattin, head of the Center for medical Image Analysis and Navigation (Department of Biomedical Engineering), is recruiting a PhD student to develop a method that is able to predict the condition of mosquito bed nets from images. The net condition is an essential factor for the planning and distribution of bed nets in the malaria prevention.
The Project
Long-lasting insecticidal nets (LLINs) are the mainstay of malaria control. However, more than 50% of people living in endemic areas are currently unprotected because LLINs often develop holes and wear out sooner than their expected lifespan. In this project, we will develop a digital tool enabling national malaria control programs to improve planning for programmatic LLIN distribution, monitoring of LLIN quality and selection of the best product for use according to contextual settings. Increasing mosquito net lifespan will optimise resource use, increase the protection of children and reduce malaria transmission.

Automatic Analysis and Prediction of Bed Net Conditions
The current estimation process of the bed net condition is performed manually according to the WHO standard. In the last years, data-driven analysis with deep learning has shown an enormous potential for automatization and prediction. An automatic estimation of the bed net condition based on images is an essential part of the project to improve and unify the estimation process of the current bed net condition. Planning the acquisition and the distribution of bed nets is often challenging due to uncertainties in the prediction of the future net conditions. Therefore, in the second part of the project, we aim to predict the future net condition based on its current condition and other impact factors.
International Cooperation
This project is an international cooperation between the University of Basel (Switzerland), the SwissTPH (Switzerland), and the Ifakara Health Institute (Tanzania). It is essential for this project that the potential PhD student travels to Tanzania for short trips to attend the work in the field and communicate the project progress. Furthermore, the PhD student will attend workshops in Tanzania to discuss details about their part of the project.

  • Master's degree in computer science, mathematics, physics, or related disciplines
  • Demonstrated and strong programming skills are necessary preferably in Python, Java, and/or C++
  • Practical background in deep learning and data-driven analysis is advantageous
  • The teamwork within the group and project partners requires excellent oral and written communication skills in English

The research will be carried out within a dynamic, interdisciplinary and international research environment. With a young, friendly, and inspiring team of PhD students and Postdocs our research group develops cutting-edge technology to push forward the fast-moving field of data analysis. You will be developing with the popular machine learning framework PyTorch . State-of-the-art computational resources will be available to you such as direct access to NVIDIA DGX-A100 clusters. Your contributions will be valued, your abilities challenged and your expertise advanced. Salary and social benefits are provided according to University of Basel rules.

Application / Contact

Please upload your complete application (cover letter, curriculum vitae, transcripts, git repositories of projects with substantial contributions and contact information of three references) until September 30th, 2022. For further information please contact Prof. Philippe Cattin ( )
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