Postdoctoral Research Assistant at ETH Zurich

ETH Zurich is offering postdoc position in mathematical optimization. Applications will be evaluated on a continuous basis.

ETH Zurich is one of the world's leading universities specialising in science and technology. It is renowned for its excellent education, its cutting-edge fundamental research and its efforts to put new knowledge and innovations directly into practice. new offered in thein theat ETH Zrich, which focuses on concepts from statistics, machine learning and mathematical optimization to describe biological systems from single cell data. The position will involve research in the interdisciplinary consortium comprising researchers at ETH Zurich, University Zurich and the University Hospitals of Zurich. Research in this consortium focuses on diagnosis of cancer patients by integrating imaging flow cytometry and deep learning approaches.

Early diagnosis of cancer is a key determinant of patient outcome. Hematological malignancies manifest themselves in the blood and are therefore amenable to blood-based diagnostics. Traditionally, such diagnostic procedures rely on manual expert microscopical evaluation of blood cell morphology and suffer from subjectivity, limited throughput and low sensitivity. This situation has motivated the ongoing transition towards molecular diagnostic assays, and shifted the challenge towards identifying suitable molecular targets. Herein, we aim to resurge morphology-based diagnosis for hematological malignancies by overcoming current limitations through the establishment of an automated procedure integrating imaging flow cytometry and deep learning to achieve objective, ultra-high throughput and sensitive diagnosis. e focus on Szary Syndrom, an aggressive cutaneous T cell lymphoma that is characterized by presence of tumor T cells with abnormal nucleus morphology in the peripheral blood.

We plan to establish a diagnosis approach based on imaging single-cell morphology of hundreds of thousands of peripheral blood cells and data-driven learning of characteristic morphologies (possibly of rare cell subsets) indicative of the presence of the disease. This approach integrates imaging flow cytometry (deMello, ETHZ) and deep learning technology (Claassen, ETHZ). The candidate is expected to develop deep learning methodologies to translate a diagnostic morphological signature for Szary Syndrome from imaging flow cytometry blood measurements. With our clinical partner (Guenova, USZ) we plan to collect peripheral blood samples from over 200 healthy and diseased donors, evaluate their single-cell morphology using imaging flow cytometry and define a disease specific morphological signature by deep learning. These activities will result in a versatile diagnostic assay, since blood-based diagnosis is minimally invasive and morphology-based diagnosis circumvents technical challenges associated with detecting molecular markers. While we initially focus on Szary Syndrome, our approach to define morphological peripheral blood cell signatures is likely applicable to a variety of other hematological malignancies or other diseases inducing morphological changes in the blood cell compartment, such as leukemia, or even inflammatory skin diseases.


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