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at the time of signing the contract. 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
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the scope of the project “COIMBRA Sustainable Tourism LLM Large Language Models applied to create better experiences for tourists and local communities, whilst supporting the development of a
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at the time of signing the contract. 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
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) 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. I.III - Ineligible candidates: Candidates enrolled in non
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of the Portuguese Catholic University: http://www.porto.ucp.pt//sites/default/files/files/DRH/RegBolsasInvestigacao_Cientifica_UCP2020.pdf Workplace: The work will be developed at the CBQF - Centro de Biotecnologia e
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of the candidate's training and experience to the objectives of the work to be developed; - Previous experience in Digital Twin, Internet of Things, Grafana and InfluxDB for creating customized dashboards and metrics
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applicable law. 3. Work plan: The selected candidate will work out R&D on ML-based Perception for AMRs targeting industrial applications. The work plan comprises the following tasks: - Development of a
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Process”, funded by the “European Regional Development Fund (ERDF), through the Northern Regional Programme (NORTE2030)”, under the following conditions: 1. Scientific Area: Electrical Engineering 2
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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, the scholarship
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Faculdade de Ciências Médicas|NOVA Medical School da Universidade NOVA de Lisboa. | Portugal | about 2 months ago
supervision of the project’s Principal Investigators: Create a spectra library of arginine methylation peptides Train transformer models to predict MSMS spectra of arginine methylation peptides. Place of work