1 Conduct a feasibility study on an after-sales car problem detection system powered by AI, using only simulation-based analysis. PFE

Overview

• Number of trainees: 1
• Conduct a feasibility study on an after-sales car problem detection system powered by AI, using only simulation-based analysis. The project focuses on designing and evaluating an AI-powered detective system for after-sales support without access to real vehicle field data.

What you will do

• Define the scope and objectives of a simulation-only feasibility study for after-sales fault detection and diagnostic systems.
• Design and run simulation experiments to generate representative fault scenarios, sensor signals and condition data necessary for training and testing AI models.
• Select and implement appropriate AI approaches (supervised and/or unsupervised) for fault detection and predictive maintenance within the simulated environment.
• Evaluate model performance using relevant metrics and produce recommendations on feasibility, limitations and next steps for real-world deployment.

Required expertise and technical tasks

• Understanding of machine learning fundamentals (supervised/unsupervised learning) and practical experience applying them to fault detection or predictive maintenance problems.
• Experience with AI models for fault detection, anomaly detection or predictive maintenance, including model selection, training and validation.
• Familiarity with data preprocessing, feature extraction, sensor signal processing and model evaluation techniques; ability to generate and preprocess simulated datasets.
• Ability to document simulation parameters, experimental setup and produce reproducible experiments and analysis.

Department, logistics and application

• Department: R&D. Subject reference: 4.
• The study must rely exclusively on simulation-based analysis; no field vehicle data will be used.
• To apply, contact HR at hr@bakomotors.com and include the project reference in your email. Number of available positions: 1.