102 machine-learning "https:" "https:" "https:" "https:" "https:" positions in Portugal
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factors: Previous experience in the construction industry; Previous experience in Building Information Modelling; Previous experience in AI and machine learning; Previous experience in process and facility
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that do not confer an academic degree, in the area or area related to that requested in the tender. Preferential factors: Have demonstrable experience in the use of machine learning algorithms applied
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an emphasis on the development of methodologies and techniques for Evolutionary Computation and Machine Learning. Work plan: Review of the state of the art in Machine Learning and Deep Reinforcement Learning
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Collaborate in the use of remote sensing and machine learning methods to detect A. longifolia and to monitor the spread and effects of the biological control agent (occasional collaboration). Activity 4
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Escola Superior de Design, Gestão e Tecnologias da Produção de Aveiro - Norte da Universidade de Aveiro | Portugal | about 2 months ago
Regulations of the University of Aveiro. 5. Work Plan: This project aims to develop solutions based on Artificial Intelligence for optimizing additive manufacturing processes. Machine learning techniques will
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, for the period from 2021 -2026. Reference: BI/UTAD/116/2025 Scientific area/research field: Engineering Researcher profile: First Stage Researcher (R1) Admission requirements: Degree in Computer Engineering
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Neuroscience on object representation and object properties using machine learning and multivariate techniques to analyze the data; support in writing scientific outputs. The grantee will also support the entire
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machine learning, particularly convolutional neural networks (CNN) and siamese networks; English language proficiency. Requirement for granting the fellowship: The applicants may apply without prior
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, of 28 of August, and also the provisions of article nº 9 of the Scientific Research Grant Regulations of the University of Aveiro. 5. Work Plan: Intelligent and modular controller with machine learning
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Faculdade de Ciências Médicas|NOVA Medical School da Universidade NOVA de Lisboa. | Portugal | 2 months ago
) Knowledge of machine learning, with the ability to support the validation and optimization of predictive algorithms applied to cardiac electrophysiology (25%); d) Knowledge of cardiac electrophysiology