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NOVEL CONCEPTS IN SUPPORT OF CLINICAL MANAGEMENT OF AMR

Statistical learning methods to predict the antibiotic resistance profile of a microorganism from its genome

The project aims to yield data management systems, cataloguing and correlating strain, genotypic and phenotypic information and its subsequent use for in-silico design of genome based molecular diagnostics. The candidate will focus on Pseudomonas resistance and its multi-factorial resistance mechanisms. More specifically, the candidate will:

  • Create a curated Pseudomonas genome database
  • Perform genome sequencing, phenotypic antibiotic susceptibility testing of representative strains
  • Design and validate a streamlined and user-friendly whole genome sequencing bioinformatic pipeline
  • Design new phenotypic and molecular antimicrobial resistance (AMR) tests
  • Perform evolutionary studies based on drug resistance patterns
  • Perform transcriptomic studies in absence / presence of certain antibiotics in different concentrations or maybe combinations.
  • bioMérieux
    BioInformatics Research Department – Grenoble
    France

 

  • Oxford University Hospitals
    John Radcliffe Hospital
    Oxford
    England

 

Specific profile requirements

  • Master degree in computer science with experience in machine learning and/or bioinformatics
  • Willingness to develop data analysis solutions for life science applications
  • Excellent interpersonal skills and ability to work with different units and departments within the organization
  • Ability to work effectively and efficiently toward goal completion in a complex, diverse and stimulating environment
  • Computational skills in standard data science programming languages (e.g., C/C++, python, R) and ability to work in an Unix environment are considered useful
  • basic knowledge of (micro)biology is a plus