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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
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