IAESTE Research Exchange Fellowships

You will contribute to an international analysis team studying biomedical Big Data sets (www.bioinf.boku.ac.at).

Typical analysis tasks include

  1. Building truly personal genome profiles for cancer prognosis
    Variation of individual human genomes was found to add over 300Mb of DNA to the human reference genome (over 10%!) making it necessary to assemble individual genomes to which known and novel genes need to be mapped. We explore the role of these truly personal genes in precision cancer prognosis.
  2. Applying Artificial Intelligence to Information Retrieval from the scientific literature to decode Drug Induced Liver Toxicity
    More than 2/3 of all drugs fail in the clinical phase due to unexpected toxicity. We are working with the US FDA on novel approaches to using Artificial Intelligence to identify and map scientific papers relevant to Drug Induced Liver Injury. On this use case, we develop and validate more generic powerful information retrieval algorithms.
  3. Exploring non-genetic sources of individuality
    We determine and investigate non-genetic molecular mechanisms that make you unique! For this we use both public data and can validate ideas in house on a Drosophila model.
  4. Pathway Analysis with Detection Bias Compensation
    All genome-scale screens exhibit a non-linear detection response. You will work with us to compensate for the resulting bias for more sensitive and accurate detection of biologically relevant patterns in genome-scale molecular profiles.

Our group also runs the world-wide Camda data analysis competition and conference to which you can contribute! (www.camda.info)

Depending on your personal interests and skills with analysis environments like R/Bioconductor, modern machine learning, scientific data analysis, or tools for next generation sequencing analysis, you will be assigned to contribute to one or two of these topics for deeper study. You will be expected to provide a concise scientific report describing your findings and conclusions as backed by solid evidence from your analysis.

Join us! We offer a first class academic research environment. We provide intense support yet require a hard work ethic and personal independence.

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