INESC TEC is accepting applications to award 1 Research Grant for MSC holders - Plurianual_25-29 - LIAAD (AE2026-0199)

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INESC TEC is accepting applications to award 1 Research Grant for MSC holders - Plurianual_25-29 - LIAAD (AE2026-0199) Sintesi in italiano (traduzione automatica): INESC TEC, un'organizzazione di ricerca con sede a Porto, Portogallo, è alla ricerca di un Ricercatore per un grant di ricerca della durata di 12 mesi, con possibilità di rinnovo. Il candidato ideale deve possedere una laurea magistrale in Informatica, Ingegneria Informatica, Data Science, Ingegneria Elettrica e Informatica, Matematica Applicata o campi affini. Le principali mansioni includono lo sviluppo di modelli predittivi e di ottimizzazione basati su intelligenza artificiale e machine learning per la gestione intelligente delle flotte. È richiesta esperienza in intelligenza artificiale, machine learning, programmazione (Python) e conoscenza di sistemi di integrazione. Il grant offre un compenso mensile di 1359,64 euro e supporto per le spese di iscrizione e assicurazione sanitaria. Portuguese version: https://repositorio.inesctec.pt/editais/pt/AE2026-0199.pdf CALL FOR GRANT APPLICATIONS (AE2026-0199) INESC TEC is now accepting grant applications to award 1 Research Grant (BI) within the scope of the Multiannual Funding of R&D Units 2025-2029, with the reference UID/50014/2025, Funded by national funds through the Portuguese Foundation for Science and Technology (FCT), I.P. 1. GRANT DESCRIPTION Type of grant: Research Grant (BI) General scientific area: COMPUTER SCIENCE Scientific subarea: Programming,Informatics Grant duration: 12 months, starting on 2026-09-01 , with the possibility of being renewed until the end of the project. Scientific advisor: Carlos Ferreira Workplace: INESC TEC, Porto, Portugal Maintenance stipend: 1359.64, according to the table of monthly maintenance stipend for FCT grants ( https://www.fct.pt/wp-content/uploads/2024/02/Tabela-de-Valores-SMM_atu… ), paid via bank transfer. Grant holders may be awarded potential supplements, according to a quarterly evaluation process (Articles 19, 21 and 22 of the Regulations for Grants of INESC TEC and Annex II), up to a maximum limit of 50 of the monthly maintenance stipend. INESC TEC supports costs with registration, enrolment or tuition fees, during the grant duration, under the terms established in the internal document: "Payment of Tuition fees to grant holders" ( https://www.inesctec.pt/pagamento-propinas-bolseirosEN ) The grant holder will benefit from health insurance, supported by INESC TEC. 2. OBJECTIVES: To develop and validate artificial-intelligence and machine-learning-based prediction and optimisation models for intelligent fleet management, capable of anticipating demand, optimising vehicle allocation and redistribution and reducing empty trips, contributing to an intelligent decision-support system that increases fleet utilisation and reduces operating costs and emissions. 3. BRIEF PRESENTATION OF THE WORK PROGRAMME AND TRAINING: 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. 4. REQUIRED PROFILE: Admission requirements: MSc in Computer Science / Informatics Engineering, Data Science, Electrical and Computer Engineering, Applied Mathematics or related fields. The awarding of the fellowship is dependent on the applicants' enrolment in study cycle or non-award courses of Higher Education Institutions. 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. Minimum requirements: 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. 5. EVALUATION OF APPLICATIONS AND SELECTION PROCESS: Selection criteria and corresponding valuation: the first phase comprises the Academic Evaluation (AC), based on the criteria referred to in Article 12 of the Regulations for Grants of INESC TEC, while the sec Annuncio in inglese. Fonte: Euraxess