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of a Research Fellowship within the scope of the project “URBAN-NET: Intelligent Networks for Carbon-Neutral and Sustainable Cities”, with operation code NORTE2030-FEDER-02697300, co-financed by NORTE
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data analysis tools. Knowledge and training in the development of communication initiatives through social networks. Organisational capacity to support project management. Publications and communications
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diplomas, if applicable. Enrolment in master. Work plan: This project aims to develop an artificial intelligence model with convolutional neural networks (CNN) trained on orthorectified aerial imagery and
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integration and thermal networks; • proficiency in Python; Minimum requirements: - knowledge in programming; - fluency in English and Portuguese (written and spoken). 5. EVALUATION OF APPLICATIONS AND
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programme data, and satellite observations in Meteorology/Earth Sciences (0-5 points); (ii) Experience with meteorological and air quality observation/monitoring station data (e.g. IPMA station network
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tests and validating numerical models. Participation in projects involving hydrogen injection and blending in high-pressure natural gas networks, will be valued. I.II - Ineligible candidates: Candidates
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of classes, using Machine Learning (ML) techniques such as Decision Trees, K-Nearest Neighbors (KNN), XGBoost, Support Vector Machines (SVM), or Neural Networks. Explore and implement clustering algorithms
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Institute of Systems and Robotics-Faculty of Sciences and Technology of the University of Coimbra | Portugal | 19 days ago
, according to the FCT's advanced human resources training regulation. Monthly Remuneration: The amount of the grant is € 1309.64 net, according to the FCT Monthly Maintenance Stiped for Research Grants for MSc
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Learning (CNN, RCNN), Large-Scale Language Models (e.g., transformer-based models such as Llama), and Statistical Network Models. The work also includes the writing of technical reports and scientific
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science/AI; Implementation methodologies of Big Data in organizational networks or ecosystems; Methods for improving business processes and operational performance, based on data on innovation and