of water microbiome diversity based on process parameters

In the last 10 years, new molecular biology tools like Next Generation Sequencing (NGS) became available to unravel the microbial composition and activity of microbial communities.  This offers the potential of providing information on both the presence and the response of the microorganisms towards their environment, allowing 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, limited number of samples, high number of unknowns in the dataset, and great disparity in the sampling frequencies.

To circumvent limitations in sample size, feature elimination techniques can be used to extract the features 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: 16.1.2023.
  • Duration: 6 months.
  • Allowance: 200 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 biostatistics.
  • Experience with R or Python is a requirement.
  • Experience with GitHub is a requirement.
  • Fluency in English.
  • Interest in water systems and in water microbiomes is an advantage.

Application form: Analysis of water microbiome diversity based on process parameters

  • Max. bestandsgrootte: 128 MB.
  • Max. bestandsgrootte: 128 MB.