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Field
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generation algorithm based on different approaches to improve understanding the behavior of forecasting algorithms in time series and tabular data. The workplan will be as follows: Literature review Design of
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, for the project/R&D institution “LASIGE”, “UID/00408/2025”, “DOI: https://doi.org/10.54499/UID/00408/2025 ”, funded by the Fundação para a Ciência e a Tecnologia, I.P./MECI through national funds under
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, Master's or Integrated Master's study cycles, and up to a maximum of 12 months for candidates enrolled in non-degree courses, scheduled to start in 2026-09. . Work plan: The general objective
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- COMPETE2030-FEDER-00728700, funded by the Foundation for Science and Technology | FCT, through the Innovation and Digital Transition Program | Compete 2030, Competition for R&D Projects in All Scientific
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months, eventually renewable, up to a maximum of 12 months. The fellowship is due to start in october 2026. . WORK PLAN: The activities to be developed include: - Producing (notably using a bioreactor) and
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, innovation, or territorial development projects, preferably funded by national or European programmes; (b) Experience in preparing technical, administrative, and financial documentation related to project
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higher education institution, and developed in association or cooperation with one or more R&D units at the starting date of the fellowship. 2. Work Plan, Tasks to perform, and objectives: This project
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Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial | Portugal | about 1 month ago
scope of the HOOPLA with the financial support of FCT/MCTES, under the following conditions: Scientific area: Master’s degree in mechanical engineering. Work plan: Development of Meso-Scale Models
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on the analysis of biological interactions in a context of environmental changes. Work Plan: Within the scope of the fellowship, the following tasks will be developed: Analysis of ecological data from field
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for urea formation through optimized catalyst design; reducing energy consumption by leveraging solar-driven PEC processes, and improving selectivity and stability, minimizing undesired by-products