of water microbiome diversity based on process parameters

Type of Project: MSc Thesis/Internship
Starting Date: Flexible
Duration: 6 months (negotiable)
Salary: 200-400 euros/month – not applicable for Erasmus student
Location: Wetsus – European center of excellence for sustainable water technology – Leeuwarden, Netherlands


In the last 10 years, new molecular biology tools became available to unravel the microbial composition and activity of microbial communities. Next Generation Sequencing (NGS) of DNA extracted from microbial populations identifies which microorganisms are present. In combination with NGS of RNA extracted from these microbial populations, an overview of the potential metabolism of the microorganisms can be obtained, providing information on both the response of the microorganisms towards their environment as well as the final chemical composition of the water as a result of their activities giving a comprehensive evaluation of the water quality in wastewater treatment plants.

The meta’omics datasets which are generated by NGS technology are big data in nature and provide an opportunity for tapping into new knowledge potential by using data-driven models. However, fitting reliable models using meta’omics dataset can be a challenging task due to high dimensionality, a limited amount of samples, high amount of unknowns in the dataset, and great disparity in the sampling frequencies of different measures in the WWTP.

To circumvent limitations in sample size, feature elimination techniques can be used to extract the feature that best model the water microbiome diversity based on an importance measure. For this internship project, a comprehensive comparative analysis of different feature elimination techniques will be conducted on a real-life data set to model water microbiome diversity as a measure of different process parameters in a saline wastewater treatment plant in the Netherlands.

Research Objective:

  • Analysis of water microbiome diversity based on process parameters
  • Appling different feature elimination techniques on a real dataset
  • Comparative analysis of the performance of the varying feature elimination techniques
  • GitHub repository maintenance for project management
  • Scientific reporting of the outcome



  • Where: Wetsus – Leeuwarden, Netherlands
  • Starting date: flexible
  • Duration: 6 months (negotiable)
  • Allowance: 200-400 euros/month – not applicable for Erasmus student
  • Experience working in an international environment
    • Experience working on a multidisciplinary project
    • Contribution to the advancement of water technology



  • EU citizen or international student registered at a Dutch university
  • Specialized in Data Science, Machine Learning, Engineering, Artificial Intelligence, Bio-informatics or bio-statistics
  • Experience with R or Python is a requirement
  • Experience with GitHub is a requirement
  • Fluency in English
  • Interest in water systems in water microbiome is an advantage

Please upload your CV (max. 2 pages) and motivation letter (max. 1 page) via the application form provided. Do not hesitate to contact Asala Mahajna ( ) if you have any further questions or you need more information.

If you have any further questions regarding this position, do not hesitate to reach out at the email above.