INESC TEC is accepting applications to award 1 Research Grant for MSC holders - CLAdvanced - CBER (AE2026-0224)

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Testo originale dell'annuncio (in inglese)

Portuguese version: https://repositorio.inesctec.pt/editais/pt/AE2026-0224.pdf CALL FOR GRANT APPLICATIONS (AE2026-0224) INESC TEC is now accepting grant applications to award 1 Research Grant (BI) within the scope of the project CLAdvanced, with reference 24765 (COMPETE2030-FEDER-02974200) Co-funded by ERDF - European Regional Development Fund through the Innovation and Digital Transition Thematic Programme (COMPETE 2030) within the scope of Portugal 2030. 1. GRANT DESCRIPTION Type of grant: Research Grant (BI) General scientific area: COMPUTER SCIENCE Scientific subarea: Computer Systems Grant duration: 12 months, starting on 2026-09-01 , with the possibility of being renewed until the end of the project. Scientific advisor: Duarte Filipe Dias 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: The Center for Biomedical Engineering Research (C-BER) has extensive experience in biomedical signal processing, particularly sound signals in clinical settings and wearable systems. This position aims to research and develop methods for processing physiological signals, including electrocardiograms and other wearable sensors, for subsequent application of machine learning and deep learning methods and classification of health and wellness parameters. Data acquisition, as well as the preparation of presentations, scientific publications, and technical reports, are also part of the objectives of this scholarship. 3. BRIEF PRESENTATION OF THE WORK PROGRAMME AND TRAINING: Processing of physiological signals (pre-processing, filtering, feature extraction in the time, frequency, and time-frequency domains). Development and validation of machine learning and deep learning models integration and analysis of data from wearable and clinical monitoring devices and clinical databases. Experimental evaluation of algorithms, development, and deployment. Support in data collection and documentation of the work performed. 4. REQUIRED PROFILE: Admission requirements: Bachelor's degree in biomedical engineering, Electrical Engineering, Computer Science, or a similar field. The awarding of the fellowship is dependent on the applicants' enrolment in study cycle or non-award courses of Higher Education Institutions. Preference factors: Previous work in developing algorithms for biomedical signal processing and machine learning/deep learning techniques with physiological signals, namely ECG. Previous knowledge in collecting physiological data and managing and preparing it for analysis. Prior experience in a hospital setting in the field of biomedical engineering. Willingness to pursue a master's thesis within the research group. Minimum requirements: Experience in biomedical signal processing. Knowledge of machine learning/deep learning. Experience in scientific programming (e.g., Python and/or MATLAB) and code management tools. Good knowledge of written and spoken scientific English. 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 second phase comprehends the Individual Interview (EI). A