PhD Thesis “Bioinformatics methods for prediction of kidney transplant survival”

Dienstort Wien Unternehmen AIT Austrian Institute of Technology GmbH
Arbeitgeber entdecken
Beschäftigungsart Teilzeit Position ohne Personalverantwortung Gehaltsangabe 2.045,00 € /brutto Jetzt bewerben

We are Austria's largest Research and Technology Organisation and an international player in the research areas that we cover. This makes us a leading development partner for industry and a top employer in the scientific community. Applications are invited for a:

PhD Thesis "Bioinformatics methods for prediction of kidney transplant survival"

50% of kidney transplants are lost within ten years. This is mainly driven by chronic antibody-mediated rejection (ABMR). Epidemiological data suggests a significant contribution of antibodies directed against mismatches, but clinical significance remains unclear. Using a systems biology approach to integrate novel multi-omics data sets into a predictive model of transplant outcome, we will be able to better match donor/receptor kidney transplant candidates.

The PhD Thesis will be done in collaboration with the Medical University of Vienna (Prof. Rainer Oberhauser) and Center for Molecular Medicine & Medical University (Prof. Christoph. Binder).


  • Analyzing peptide and protein chip data
  • Development of bioinformatics pipelines for LoF and nsSNPs identification
  • Creating methods for selecting/screening epitopes for peptide arrays
  • Establishing a data platform for integration of factors leading to kidney organ rejection

Candidate profile

  • Master in bioinformatics, computational biology, computational chemistry or related field
  • Knowledge about protein structures, protein interactions, epitope prediction, variant annotation
  • Experience with programming languages and Linux command line tools
  • Know-how in data analyses and data integration

Duration: 3 Years

Your compensation:
AT least € 2.045,10 gross for 30 hours per month standard personnel salaries for FWF project proposals.

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PhD Thesis “Bioinformatics methods for prediction of kidney transplant survival”

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