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Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The activities proposed in this call aim to develop and
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interest to individuals in frontline health and care roles who have had limited access to research training and who wish to develop a long-term interest in mental health research. Eligible courses include
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process development, optimization, maintenance, and communication. As a member of the leadership team, responsible for conducting the following activities on behalf of the Financial Aid function: Planning
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the European Regional Development Fund (ERDF) through the NORTE2030 Operational Program, underway at the Faculty of Fine Arts of the University of Porto, under the following conditions: 1. Research area/s: Art
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interviews is open to award no. 1 research grant in the field of “Analysis of high-resolution morpho-bathymetric and seismic data for realizing the CARG geological sheet No. 627 “Selinunte” and development
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. or - Holder of an academic degree enrolled in a non-academic degree course integrated in the educational project of a higher education institution, developed in association or cooperation with one or more R&D
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wording, provided that, under subparagraph e) of Article 3 of RBI-FCT, they are developed in association or cooperation between the higher education institution and one or more R&D units. [JB2] I.III - [JB3
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their scientific training through the development of research work leading to the award of the respective academic degree, whether or not integrated into R&D projects. 2 ) Be a master’s degree holder enrolled in non
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the educational project of a higher education institution, developed in association or cooperation with one or more R&D units. Note: In the case of masters who are enrolled in non-academic degree courses
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | about 1 month ago
, reference no. 2023.18249.ICDT, financed by national funds through FCT/MCTES (PIDDAC Workplan: Development of methodologies and machine learning algorithms for the detection of anomalous behaviors in flow rate