Internship in Machine Learning on Graph Anomaly Detection (M/F)

Reference : ITIS-2020-Intern-010

Type: Intern
Contract type: Internship
Duration: 6 months
Place: Belval



Your work environment

The Luxembourg Institute of Science and Technology (LIST) is a Research and Technology Organization (RTO) active in the fields of materials, environment and IT. By transforming scientific knowledge into technologies, smart data and tools, LIST empowers citizens in their choices, public authorities in their decisions and businesses in their strategies.

You will be part of the LIST IT for Innovative Services department

With a team of more than 100 highly skilled researchers and engineers from various disciplines, the ITIS Department of the Luxembourg Institute of Science and Technology (LIST), addresses the technological, organizational, human, and economic aspects of innovative IT services. Research areas are based on the innovation of services with a high level of information intensity and quality, allowing trust to be gained in their use and value to be generated around new business models.




Fake online reviews in e-commerce significantly affect online consumers, merchants, and market efficiency. The rise of fake news and artificial online reviews in social media are serious threats to the privacy and security of today's societies, economies, and democracies. These data security issues require specific data mining and machine learning tools to be detected, processed, and managed efficiently and in real-time. Within the TSS department of ITIS, the intern will have the responsibility of performing high quality applied research in the fields of machine learning (ML) on networks, i.e., large scale graphs.

The research topic is related but not limited to the implementation of selected state-of-the-art ML algorithms in the field of anomaly detection on networks modeling e-commerce systems or social networks.





The intern is expected to accomplish the following tasks:

  • Recopilation and surveying of relevant literature in the field of anomaly detection on networks
  • Identification of relevant real-life datasets for benchmarking purposes, i.e., Tweeter, Reddit, Amazon, Booking data
  • Designing and implementation of a proof of concept simulator in anomaly detection on networks applied on real-life datasets
  • Scalability of the identified algorithms using distributed/parallel computation
  • Documenting and communicating the obtained results

The candidate should perform his/her research, envisaging a future publication in any international conference or journal.

Language skills

  • Be fluent in English (spoken and written)
  • Knowledge of French is a plus

Possible specific skills/knowledge required

  • Master's student in mathematics, applied mathematics, computer science, or statistics
  • Strong programming skills in Python or R
  • Latex



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 Efrain Leonardo GUTIERREZ GOMEZ