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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | about 2 months ago
, reference no. 2023.18249.ICDT, financed by national funds through FCT/MCTES (PIDDAC Workplan: Development of methodologies and machine learning algorithms for the detection of anomalous behaviors in flow rate
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“Enhancing Machine Learning Approaches for Spatially Dependent Data in Fisheries and Environmental Research” (CMAT, University of Minho), reference 2024.15617.PEX, financed by national funds through
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to express their opinion, in a preliminary hearing. Where to apply Website https://www.ipn.pt/bolsas Requirements Research FieldEngineering » Computer engineeringEducation LevelBachelor Degree or equivalent
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: Academic performance in courses within the fields of Programming, Artificial Intelligence, Machine Learning, or related areas – 40%; VII.II- I – In the evaluation of the interview, candidates' performance
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%; - Criterion 2: Scientific dissemination actions – 40%; - Criterion 3: Academic performance in courses within the fields of Programming, Artificial Intelligence, Machine Learning, or related areas – 20%; VII.II
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machine-learning methods for sample segmentation and classification. 3. BRIEF PRESENTATION OF THE WORK PROGRAMME AND TRAINING: The fellow will join the INESC TEC team within the LIBScan project, carrying
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behavior of these components will be developed based on Finite Element Methods (FEM) complemented by Machine Learning models. Legislation and Regulations: Statute of Scientific Research Fellow, approved by
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(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
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tasks in which the candidate was involved). Proven knowledge and experience in the use of qualitative and quantitative research methodologies. Experience in using computer tools, namely SPSS and NVivo
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and signature matching based on existing code. - Performance evaluation of the application on a Raspberry Pi (RPi). - Development of improvements to machine learning algorithms for anomaly detection and