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Intelligence (AI) algorithms, including Machine Learning (ML) and Deep Learning (DL) techniques, for advanced signal analysis. The work will focus on developing methodologies for the detection, extraction
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, with applications ranging from scientific research to medical imaging and marketing analysis. With the ever increasing amount of learning data, these algorithms face computational challenges
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Development Fund (FEDER), through the National Innovation Agency (ANI), the Framework Programme Portugal 2030 – Programa Inovação e Transição Digital | COMPETE 2030, medida eixo SIID – Internacionalização de I
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the numerical modelling of thermal phenomena, with particular emphasis on solidification processes. d) Knowledge on machine learning methods or data-driven modelling approaches applied to materials science or
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sign this declaration. (Place), (date). __________________________ (full name) Where to apply E-mail [email protected] Requirements Research FieldBiological sciencesEducation LevelMaster Degree
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learning-assisted computational pipeline for the automated detection of point defects in atomic-resolution scanning transmission electron microscopy (STEM) images. Using monolayer MoS₂ as a model system, the
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of Mechanical Engineering, University of Minho; e) Substitute Member: Dr. Sara Cristina Soares Madeira, Entry-level PhD researcher at the Centre for MicroElectroMechanical Systems (CMEMS-UMinho), University
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d) Substitute Member: Doctor João Pedro Mendonça Assunção Silva, Assistant Professor at the Mechanical Engineering Department, University of Minho e) Substitute Member: Doctor José Manuel Ramos Gomes
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of knowledge, by the Regulation of Scientific Employment (RSE), by the Regulamento da Carreira, Recrutamento, Contratação e Avaliação do Desempenho do Pessoal Investigador em Regime de Direito Privado da UMinho
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Sistema Avançado melhoria do desempenho da qualidade do ar interior aplicado aos sistemas de AVAC para as unidades e infraestruturas de saúde, (AT AirHealth2024), financed by national funds through Agência