Postdoctoral Research Assistant
|Workplace||Zurich, Zurich region, Switzerland|
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.The position is offered in the Computational Biology (Claassen) Group in the Institute of Molecular Systems Biology at ETH Zürich, 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 builds on recent advances in immunotherapy in the treatment of cancer patients.
Cancer immunotherapy has revolutionized the treatment of cancer patients and promises to be the biggest game changer in modern medicine. Immune responses in general are self-limiting to avoid unnecessary tissue destruction when combatting a pathogenic invader. This regulatory circuit is well controlled and balances effective pathogen defenses with the maintenance of normal tissue functions. Immunotherapy seeks to drive immune responses against cancer by interfering with the normal regulatory functions of immunity. These "checkpoint inhibitors" are now used to treat several cancers, but were particularly successful in metastatic melanoma, where patients have seen remarkable and durable responses. However, not all patients benefit from these groundbreaking treatments, and given the 3-4 months required to assess response (as well as the high expense and incidence of adverse events), it is essential to personalize therapeutic options according to the individual patient. In this way, we hope to identify "predictive biomarkers" that will more effectively optimize therapy options and increase the chance of controlling the disease for each patient.
We have assembled a unique and innovative consortium that integrates an advanced biomarker discovery pipeline through the combination of detailed and comprehensive measurements of patient blood profiles, as well as the quantitative analysis of radiology images and machine learning approaches. We will use all these data to develop better predictive and prognostic biomarkers and treatment algorithms for cancer immunotherapy, with the goal of ultimately extending the benefit of the latest cancer drugs to as many patients as possible. The candidate will apply and develop computational approaches to identify therapy response associated cell subsets and instruct further in-depth experiments for these cell subsets. Upon validation in independent patient cohorts, this information will then be used to stratify patients prior to immunotherapy to maximize the therapeutic impact and to minimize adverse effects as well as to provide new personalized therapeutic targets for immune-intervention across the Swiss wide patient population.
The ideal candidate brings along a degree that demonstrates an interdisciplinary background in both life and formal sciences. While a background in experimental biology is highly recommended to carry out the envisioned single cell proteomic experiments, a solid background in mathematics, statistics and programming is required to carry out the subsequent data analysis. A fluent level of English is mandatory. We are looking for a highly motivated candidate with excellent communication skills that is capable of working in an interdisciplinary environment and can team up with scientists for experimental as well as computational analysis. The candidate should have a high degree of initiative. A competitive salary will be provided, which will be well matched relative to the cost of living in Zürich. We offer work in a highly stimulating environment with state-of-the-art infrastructure, providing the successful applicant with unique opportunities to develop a strong interdisciplinary portfolio in both experimental and computational biology.
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