Master Student in visual analytics for agricultural data analysis (M/F)

Reference : ERIN-2018-INTERN_002

Type: Intern
Contract type: Internship
Duration: 6 months (6 months during Spring/Summer 2018)
Place: Belvaux



he "Environmental Research and Innovation" (ERIN) department of LIST has an opening for an Intern position. The intern will be integrated in the e-Science unit, which focuses on data analytics & visualization and their applications in various environmental domains.




In many real-world applications, exploratory analysis of multiple sets of categorical entities with close relationships is a critical task. For the further digitalization of -for instance- agriculture, databases referencing thousands of products containing often the same combinations of active ingredients used for multiple purposes need to be visualized for enabling informed decision making by the users of technologies related to the database entries. Discovering and understanding such close relationships offers new possibilities to agricultural stakeholders.

Relationships between two types of entities (e.g. pesticides active ingredients) can be modelled in a matrix from which biclustering algorithms extract co-occurrence patterns shared by multiple entities (biclusters). For instance, from a product <=> ingredient matrix, a bicluster groups a subset of ingredients that co-occur in a subset of products. However, visualization of overlapping biclusters remains a challenging issue, specifically for identifying the common and specific parts of a large amount of biclusters. To this end, the eScience unit of LIST has proposed an approach applied on a term<=> document matrix in a visual analytics tool for text corpora analysis. This approach needs to be improved and adapted to relevant agricultural cases for unlocking the full potential of digitalization in precision agriculture with regard to sustainability gains.

During this internship, the candidate will start by making a brief literature review of bicluster visualizations. Next, she/he will work closely with researchers of LIST to adapt existing approaches and/or propose a new one for the visualization of biclusters. The interactive visualizations will be implemented with D3.js as a web-based application supporting the tasks of agronomists. Finally, a user study will be conducted to compare efficiency of these different approaches with regard to the most relevant tasks.




Education and Competencies

  • Master of Science in Computer Science or equivalent in progress
  • Proficient in at least one of the following programming languages: Java, Python, Javascript
  • Knowledge in data mining and data visualization is a plus.
  • Knowledge in the following technologies is a plus: D3js, Angularjs.
  • Self-organized, team worker, with good communication skills.
  • Basic knowledge of production processes in farms, orchards or vineyards will be considered as an asset


English is mandatory, French and/or German is a plus


Candidates interested in the above position can apply online, directly on this webpage.
The application file should include:

  • A CV
  • A motivation letter
  • A transcript (a copy of the student’s grades)

For more details about the scientific content of the internship, please send your inquiries to


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Dr Nicolas MEDOC