Master thesis / Semester project

Cell imaging-based diagnostic platform for patients with rheumatic diseases

We are looking for a motivated student who is skilled in machine learning and algorithm development to explore our large multi-fluorescence imaging datasets at the single cell level, to build and validate a computational workflow for automated image analysis.

Our aim is to create a diagnostic platform based on single-cell imaging to characterize the functional stage of synovial fibroblasts (SF) from patients with rheumatic diseases and predict an efficient, tailored, in vitro tested treatment as part of our precision medicine approach to improve the course of rheumatic diseases. The core function of the platform is the classification of SFs based on microscopy image data, which has been generated using routine assays to measure various physiological processes in SFs (e.g. mitochondrial activity, oxidative stress and apoptosis).

The master student will apply machine-learning (ML) techniques to the analysis of SF microscopy data. The project will have both a supervised and unsupervised component. Unsupervised analysis will allow for the image-based characterization of SFs, such as subgrouping based on physiological processes. The supervised analysis will enable automated classification of SFs. The student will develop robust image processing pipelines and computational workflows that will facilitate the pre-processing, visualization and classification of SF image data.

The project is located at the Centre for Experimental Rheumatology, a European League against Rheumatism (EULAR) Centre of Excellence, at the Schlieren campus. Data analysis will be conducted jointly with the lab of Prof. Krauthammer, located at central hospital campus.

Tasks: 100% data analysis Project start: Immediately or on agreement. Contact Details: Dr Eva Camarillo Retamosa: Eva.CamarilloRetamosa@usz.ch, Prof. Caroline Ospelt: Caroline.Ospelt@usz.ch

Center of Experimental Rheumatology, Department of Rheumatology, University Hospital Zurich Prof. Caroline Ospelt and Dr. Eva Camarillo

In collaboration with the Chair of Medical Informatics, Department of Quantitative Biomedicine, University of Zurich Prof. Michael Krauthammer, Dr. Ahmed Allam and Mr. Xiaochen Zheng (PhD student)

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Ahmed Allam
Senior Researcher

Apparently, this member prefers to keep an air of mystery about them.

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