Artificial Intelligence
[Open soon]
Work description
State-of-the-art review of artificial intelligence and machine learning applied to fleet management and optimisation; identification and characterisation of critical operational, energy and sustainability performance parameters; formulation of the conceptual predictive fleet-optimisation model; development and tuning of demand-forecasting, redistribution and maintenance algorithms (supervised and unsupervised learning, neural networks, gradient boosting and reinforcement learning); construction of the dataset and simulated training library and definition of performance metrics (RMSE, accuracy); training and validation of the models in a controlled environment and on a pilot fleet; and support for the integration of the decision-support system with the management platforms (ERP/CRM) and interoperability APIs.
Academic Qualifications
MSc in Computer Science / Informatics Engineering, Data Science, Electrical and Computer Engineering, Applied Mathematics or related fields.
Minimum profile required
Experience in artificial intelligence, machine learning and data science; programming skills (Python and machine learning libraries); experience with large language models (LLMs) and retrieval-augmented generation (RAG) architectures; experience with time series, operational data and optimisation problems; knowledge of systems integration (ERP/CRM, APIs); expert with Docker.
Preference factors
Completed MSc in one of the indicated fields and enrolment (or eligibility for enrolment) in a non-degree-conferring course, under the Research Fellowship Holder Statute; programming and machine learning skills.
Application Period
Since 17 Jul 2026 to 30 Jul 2026
[Open soon]
Centre
Artificial Intelligence and Decision Support