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+ Inovação e Sustentabilidade na Gestão dos Recursos Marinhos da Região Centro”, financed by the European Regional Development Fund (ERDF), through the Regional Programme of the Centre (CENTRO2030) of Portugal
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the European Regional Development Fund (FEDER), through COMPETE2030, under the following conditions: Type of Grant: Research Grant (BI) – PhD level Scientific Area: Microbiology, Bioinformatics and Computational
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Development Fund (ERDF) of the European Union, through the COMPETE2030 of Portugal 2030 - COMPETE2030-FEDER- 00825300 (projet FCT nrº 16965)1 3. Admission Requirements: 3.1. 3. Students enrolled in doctorate
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the Directorate-General for Higher Education (Direção Geral do Ensino Superior, DGES: only in the case of automatic recognition). Candidates should consult the DGES portal for more information at https
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exclusivity: The PhD Researcher perform research activities, supervision and management of several tasks planned project, namely: development of cellulosic derivatives and nanocellulose-based composites
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) The recruitment procedure for workers in a professional development situation, as per Article 265 of the LTFP. 5th - Only national, foreign and stateless candidates holding a PhD in Biology or related fields may
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of FCiências.ID - Association for Research and Development of Sciences at https://fciencias-id.pt/ . XI. Compliance with public policies FCiências.ID actively promotes a policy of non-discrimination and equal
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-phase, solid-state, soft ionic and nanostructured materials; (ii) experience in the use of MD (Gromacs, LAMMPS) and ab initio MD (VASP, CP2K) software packages; (iii) knowledge of force-field development
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Validation at the UTAD Ecocampus.” The Researcher will perform the following duties: i) Conduct research focused on the development and evaluation of innovative solutions aimed at reducing the impact of
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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