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public tender is open for the attribution of 1 (one) Research Grant under the Scientific Research and Technological Development Project entitled SONORA - Towards safe, optimized and personalized radiology
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within the framework of project PROMisH - Potential of Rheologically-Optimized Magnetic Emulsions in Hemotherapy, with nº 16207, and the operation code at the Funds Platform COMPETE2030-FEDER-00736400 and
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, as well as the investigation and optimization of enzymatic treatments applied to almond flours, with the goal of reducing the antigenicity of proteins and contributing to the development of a
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at the development and optimization of sustainable biocatalytic strategies applicable to the recycling of plastic materials. Within the scope of the work plan, the selection of appropriate enzymes will be studied, as
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de Campos IV - Work Plan / Goals to be achieved: Activity 1 – Optimization of the rubber solution formulation and application This activity involves improving the properties of the dissolved rubber
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samples. The candidate will be involved in developing, optimizing, and validating advanced analytical methodologies, applying them to analyze environmental samples, processing and interpreting data using
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relationships, this project aims to elucidate the mechanisms governing hydrogen uptake and release, ultimately contributing to the rational optimization of materials for efficient, safe, and controllable hydrogen
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Escola Superior de Design, Gestão e Tecnologias da Produção de Aveiro - Norte da Universidade de Aveiro | Portugal | 3 months ago
Optimization of part design based on computational technologies Manufacturing of proof-of-concept parts using AI-optimized parameters for iSLS Post-processing of proof-of-concept parts Participation in
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Escola Superior de Design, Gestão e Tecnologias da Produção de Aveiro - Norte da Universidade de Aveiro | Portugal | 3 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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planetary boundaries (a set of at least nine parameters that indicate how far the ES is from its optimal functioning state during the Holocene (about 12,000 years ago). This model is inspired by Matrix Models