INESC TEC is accepting applications to award 1 Research Grant for MSC holders - DEFLECT - HUMANISE (AE2026-0230)

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Testo originale dell'annuncio (in inglese)

Portuguese version: https://repositorio.inesctec.pt/editais/pt/AE2026-0230.pdf CALL FOR GRANT APPLICATIONS (AE2026-0230) INESC TEC is now accepting grant applications to award 1 Research Grant (BI) within the scope of the within the scope of the project DEFLECT with reference 17185 (COMPETE2030-FEDER-00875500) co-funded by the ERDF - European Regional Development Fund through Innovation and Digital Transition Program - COMPETE 2030 under the scope of Portugal 2030 and by National Funds through the FCT - Fundacao para a Ciencia e a Tecnologia, I.P. (Portuguese Foundation for Science and Technology). 1. GRANT DESCRIPTION Type of grant: Research Grant (BI) General scientific area: COMPUTER SCIENCE Scientific subarea: Informatics Grant duration: 6 months, starting on 2026-09-01 , with the possibility of being renewed until the end of the project. Scientific advisor: Hugo Paredes 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 understand the challenges associated with the collection, quality and analysis of longitudinal health data; • To develop skills in the design, implementation and evaluation of data science-driven approaches applied to healthcare; • To explore approaches suitable for analysing incomplete and/or distributed data, using techniques such as imputation or federated learning; • To adopt good research and development practices, ensuring methodological rigour, reproducibility, documentation and critical analysis of the limitations of the methods used; • To apply critical thinking when evaluating the research process and the results obtained; • To prepare project documentation and scientific publications.; 3. BRIEF PRESENTATION OF THE WORK PROGRAMME AND TRAINING: The work plan includes a review of the state of the art on data science approaches applied to healthcare, as well as the study of the main challenges associated with the collection, preparation, quality and analysis of longitudinal data. Approaches suited to the problems and data considered within the scope of the project will be designed, selected and implemented. The work may include techniques for imputing missing data or federated learning approaches for analysing distributed data. The approaches developed will be systematically evaluated and compared, considering their performance, suitability for the context, robustness and limitations. The work will also include the critical analysis and discussion of the results, the adoption of practices that promote reproducibility, the documentation of the activities carried out, and the preparation of reports and scientific publications. 4. REQUIRED PROFILE: Admission requirements: - Master’s degree in Computer Engineering or a related field, with a final grade of 16 out of 20 or higher. The awarding of the fellowship is dependent on the applicants' enrolment in study cycle or non-award courses of Higher Education Institutions. Preference factors: - Experience in designing and developing databases for heterogeneous data; - Experience participating in research projects; - Ability to collaborate and interact with different types of partners, including industry partners.; Minimum requirements: - Publication of at least four peer-reviewed scientific