The Department of Biomedical Engineering contributes to a better future in meeting health care needs by innovative biomedical and drug-discovery 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 Idorsia Pharmaceuticals Ltd., Professor Philippe Cattin, head of the Center for Image Analysis and Navigation (Department of Biomedical Engineering), is recruiting a PhD student to study confounding factors in machine learning predictors targeted to drug-discovery applications and includes function prediction of compounds and histopathologic image segmentation.
Data-driven analysis with deep learning offers an enormous potential in today's life-science sector. Trained end-to-end, deep learning models exceed human-level performance. However, controlling for confounding factors is not straight forward. Is it a bias in an acquisition batch or other hidden correlations of the input data and the target, confounding variables need to be controlled to make meaningful predictions. The project will cover a multitude of data modalities from large biological and chemical data sets of high-content screenings, whole-slide images, and genomics.
Acquisition Batches in Biological Activity Prediction
Capturing the morphology of a cell from microscopy images offers great potential for predicting its biological activity. Exploiting predictive features, so called image-based compound fingerprints, may increases hit rates by orders of magnitude. Despite their potential, in practice, the biological activity severally correlates with acquisition batches which need to be considered as confounds to make accurate predictions. You are going to be working with public data sets such as the Compound Profiling Data Set BBBC021 and the 1008 Tales Data Set , and the “Phenoprint" data set acquired by our industrial partner which is based on https://doi.org/10.1016/j.chembiol.2018.01.015 .
Histological Whole-Slide Images
The automatic analysis of histological whole-slide images is ever more relevant in digital pathology. In a wide variety of challenges ranging from tissue detection and characterization and understanding of cell interactions, confounds ought to be identified and their influence alleviated to reach high-quality estimates. You will handle and analyze giga-pixel images from several studies which are conducted in-house and heterogeneous sets of oncological data of human trials acquired on multiple sites.
- 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 on an industry project basis within a dynamic 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 and contributing to open-source software. State-of-the-art computational resources will be available to you such as direct access to NVIDIA DGX-A100 clusters and cloud computing resources via Amazon Web Services. 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, git repositories of projects with substantial contributions and contact information of three references). For further information please contact Prof. Philippe Cattin (email@example.com )
Our website: https://dbe.unibas.ch
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