3-year PhD Position in Machine Learning (Federated Learning)

University of Mons - Department of Computer Science - Belgium

Open PhD Position

We seek applications for a three-year PhD student position in machine learning with a focus on federated learning, time series modelling, and predictive maintenance. The PhD candidate will be involved in a three-year research project in collaboration with various academic and industry partners. She/he will be part of a machine learning research team and will contribute to the participatory research process, collaborating directly with the industry partners. The main objectives of the project is to develop new federated learning models and algorithms for predictive maintenance based on time series data. Training machine learning models in a federated manner can involve various statistical and system challenges, including data heterogeneity, privacy constraints, model robustness and security, communication efficiency, system heterogeneity, and dynamic environments. The PhD candidate will be supervised by Souhaib Ben Taieb (PhD, Associate Professor), working at the Department of Computer Science of the University of Mons. Belgium is centrally located in Europe, and the lab is well-connected to other research teams worldwide. A research position in our group is an ideal stepping stone for an independent research career in academia or industry.

Eligibility details and applications

Qualified candidates should hold a Master’s degree or equivalent in computer science, statistics or related domains, with a background in machine learning. A good knowledge of statistics/machine learning and former experience in data analysis with popular ML Python libraries (e.g. Scikit-learn, Pytorch, TensorFlow) are highly recommended. Candidates should be proficient in English and have good oral and written communication skills. Interested applicants should contact the principal investigator by e-mail at souhaib.bentaieb@umons.ac.be. Official applications should be submitted at your earliest convenience and should contain at least:

  • motivation letter
  • the earliest available starting date of the candidate
  • a CV, including previous experience relevant to the project
  • a list of previous publications (if applicable)
  • a digital copy of the master thesis
  • a copy of relevant grade documents
  • full contact details of the candidate
  • contact information for at least two potential academic referees