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- Universidade de Coimbra
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; - Development of a computational model capable of enriching SVG/JSON vector formats; - Development of a computational model capable of transforming the components of a graphic object at the vector level
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- Work Plan / Goals to be achieved: Objectives: Investigation of state-of-the-art computer vision models for segmentation and classification. In addition to knowledge in the scientific domain
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potential. General scientific area: Computer science, business sciences or similar. Where to apply Website https://www.ipc.pt/bupc/concursos/form/concursos_bolseiros Requirements Research FieldComputer
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in animal models (mice) of obesity, including biodistribution and pharmacokinetic evaluation, as well as photodynamic therapy application. -Participation in science outreach activities. V - Initial
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Photoshop; Development of maps, maquette, digital terrain models (DTM), and other graphic elements for landscape representation; Preparation of written and graphic materials for workshops and organization
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operational program. The publicity rules are available on the FCT website, as well as on the websites of the funding Operational Programs, if applicable. The use of the FCT logo available at http://www.fct.pt
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, the work plan includes the following tasks: i. Development of models for planning and scheduling maintenance tasks, applied in the scope of aeronautical maintenance. ii. Implementation of the models
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Simões da Silva IV - Work Plan / Goals to be achieved: Title: Benchmarking of language models in Portuguese Objective: Collection and structuring of data in Portuguese covering different scientific and
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); Regulamento de Bolsas de Investigação da FCT,l.P. was approved on the December 16 of 2019, which full text is available at: https://dre.pt/application/conteudo/127238533  ; and in accordance with
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | about 1 month ago
, promoting trustworthiness. Four research profiles are available under this context: Profile 1 (P1) – Development of methodologies based on graph neural networks and foundation models to integrate multiple