53 evolution "https:" "https:" "https:" "https:" "https:" "https:" "Babes Bolyai University" scholarships at Universidade de Coimbra
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development of activities related to the Soils for Europe project (www.soils4europe.eu ).) V - Initial grant duration: 12 months V.I - Renewal Possibility: Possibily renewable VI - Funding and financial
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fermentation processes. 3 – Characterization and preparation of natural-based textile fibers for dyeing. 4 – Development and optimization of textile dyeing processes using microbial and natural-based dyes. 5
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expected to support the development of tasks within the METAPORCONCRETE project, namely: - studies for defining acoustic metamaterial solutions, including porous materials (porous concrete); - support in
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qualifications. VII.VIII - All provisional and final jury decisions must be justified, recorded in minutes, and published on https://apply.uc.pt/ . VII.IX - Jury Composition: President: Maria Manueloa Monteiro
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on the development of nanoformulations for the cell-specific delivery of diverse RNA payloads aimed at inducing cardiac regeneration. The work is conducted within the framework of a transnational research project
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justified, recorded in minutes, and published on https://apply.uc.pt/ . VII.VII - Jury Composition: President: Joel Alexandre da Silva de Jesus Effective Members: Ricardo Nuno Madeira Soares Branco and Dulce
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. VII.VIII - All provisional and final jury decisions must be justified, recorded in minutes, and published on https://apply.uc.pt/ . VII.IX - Jury Composition: President: Isabel da Silva Henriques Effective
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supervision/coordination of the grant: Luís Maria Marques dos Santos Bimbo IV - Work Plan / Goals to be achieved: The selected candidate will integrate experimental development activities aimed at developing
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of Coimbra III- Scientific supervision/coordination of the grant: Rui Paulo Pinto da Rocha IV - Work Plan / Goals to be achieved: 1. Development of algorithms for swarm robotics and human–swarm interaction 2
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objectives: 1 – Development of a tool for identifying operating regimes using machine learning techniques. 2 – Development of a tool for identifying the causes of process eco-efficiency degradation using