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Master internship

France, VILLEURBANNE · Job Posted December 14, 2025
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Job Description

FUTURE project aims to develop an uncertainty-aware and trust-oriented decision-making framework combining uncertainty modelling, AI-based predictions, and Dempster–Shafer theory. By integrating belief-function reasoning with multi-source evidence fusion, FUTURE enhances the resilience of CPS decision pipelines. A realistic CPS scenario specifically, a Smart Building environment will serve as the experimental validation context, leveraging sensor data to evaluate uncertainty, reliability, and evidence-based decision performance.

Job Responsibility

  • Data Importation
  • Data Cleaning, Normalisation, and Harmonisation
  • Uncertainty Modeling of Data
  • Source-Aware Categorisation of Data
  • Development of an Uncertainty-Aware AI Model
  • Transformation of Model Outputs into Belief Masses
  • Evidence Fusion
  • Decision-Making under Uncertainty

Requirements

  • Master student or equivalent in Software Engineering or Computer Science
  • Knowledge and experience in Probability, uncertainty modelling, and evidence-based reasoning
  • Fundamentals of machine learning and classification
  • Understanding of CPS architectures
  • Python programming (NumPy, SciPy, Pandas)
  • PyTorch or TensorFlow for deep learning
  • Dempster–Shafer libraries (e.g., pyds)
  • Data cleaning techniques
  • Git version control
  • Analytical thinking and autonomy
  • Problem-solving
  • Ability to work in interdisciplinary teams
  • Scientific communication and reporting

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