Postdoctoral research position: Advanced statistical / machine learning analysis of antimicrobial resistance data

Country: South Africa;

City: Stellenbosch

Vacancy added: 31.05.2019

Employer: Stellenbosch University

Resumes due: 01.07.2019

 

Title: Postdoctoral research position: Advanced statistical / machine learning analysis of antimicrobial resistance data

Please note that postdoctoral fellows are not appointed as employees and are therefore not eligible for employee benefits. Postdoctoral fellowships are also awarded tax free.

The duration of the research position is 24 months, subject to performance reviews.

The inherent complexity of antimicrobial resistance (AMR) requires novel approaches to analyse data from AMR monitoring operations: these include statistical/machine learning techniques in combination with domain knowledge. The goal of this research project is to evaluate existing data regarding the prevalence of AMR in the environment, clinical cases of resistant infections, and more, with the purpose of obtaining a deeper understanding of AMR epidemiology.

The main role of the postdoctoral fellow will be to collect data from relevant parties, analyse the data using advanced statistical/machine learning techniques, and develop data visualisation strategies that can be used to communicate the true impact of AMR to the general public.

The research is funded by Grand Challenges Africa, in collaboration with the Medical Research Council and the Bill & Melinda Gates Foundation. The research will complement an existing collaboration between Stellenbosch University and the University of Bath.

 

Duties:

  • Collect clinical data from the National Institute of Communicable Diseases and other relevant stakeholders;
  • Collate relevant monitoring data generated during previous research projects;
  • Consult with experts in the field to develop domain knowledge;
  • Analyse collected data using relevant statistical/machine learning techniques;
  • Assess the predictive potential of datasets collected in resource constrained settings.

For more information about the research group and the Department of Process Engineering, please read here.

 

Requirements:

  • A PhD obtained within the last five years in the field of statistics, applied mathematics, microbiology, or related field, with publication track record in international scientific journals in the relevant field;
  • Applicants with a PhD in statistics, applied mathematics, or a relevant field, with an interest in microbiology and a desire to develop an understanding of healthcare systems and antimicrobial resistance; or
  • Applicants with a PhD in microbiology and a focus on AMR, with a strong mathematical and/or statistical background and a desire to develop a statistical/machine learning skillset.
  • Strong programming skills (preferably in MATLAB/R/Python).
  • Strong technical, analytical and communication skills.
  • Willingness to engage with relevant stakeholders.
  • The ability to “think outside the box” in terms of collecting and analysing large datasets.

Application deadline: 1 July 2019

 

Please submit your complete application (including cover letter, CV with details of at least two referees who may be contacted, relevant degree and other certificates) by 1 July 2019 to:tmlouw@sun.ac.za

 
Where to send resume: