Recently, an excessive boost in the development of analytical bioinformatic algorithms allows biologists to mine the available datasets originating from single cells in a completely unsupervised manner for the first time. Human body is composed of around 30 trillion cells and the objective of this application is the use of novel dimensionality reduction, trajectory inference and clustering algorithms in order to decode their directional relationship in the field of systems immunology. Our research focuses on dynamic systems of immune response monitoring in patients suffering from infectious diseases – tuberculosis or borreliosis – as well as various solid tumors – mainly bladder cancer. Under these pathological conditions an activation of immune cell subsets of both innate and adaptive systems regulate the outcome of the disease. A single-cell oriented statistical data evaluation and visualization finally stratify each patient. We are going to optimize parameters of the non-linear computational methods in order to preserve data distribution and maximize reproducibility.

An inherent part of the workflow is the collection of genomic/transcriptomic/proteomic data and a patient database maintenance. The predictions of directional causal relationships will be validated in available zebrafish and/or medaka models after multiparametric flow cytometry and cell sorting in our laboratory at the level of gene and cellular networks. A collaboration with local clinical partners (TH, HNB, Prague) and computation centers (IDA FEE CTU1, IOCB CAS2, Prague) is backed by recent publications. International collaboration within LifeTime consortia will be an inherent part of the project. 

A deep knowledge of R and Python (C++ is a bonus) and immunology is an essential profile of a successful candidate. Optionally, the experience with cytometry and/or microscopy and the knowledge of one of the experimental models is a plus. Two major goals of this PhD position are: 1/ the integration of existing tools into a Galaxy pipeline or development of a standalone application and 2/ the identification of cellular biomarkers of either latent TB infection, late-stage Lyme disease or bladder cancer stem cells.

1. Dvorakova, E. et al. Bioinforma. Res. Appl. ICBRA 2019 (2019). at

2. Kratochvíl, M. et al. bioRxiv 496869 (2019).doi:10.1101/496869

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