96 algorithm-development-"Prof"-"Prof"-"Washington-University-in-St" Fellowship positions in Portugal
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call for application is now open for the attribution of 2 (two) Research Fellowships with reference 85/2025/BI/AgendasPRR, for development of scientific and technological research activities, under
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call for application is now open for the attribution of 1 (one) Research Fellowship, with reference 86/2025/BI/AgendasPRR, for development of scientific and technological research activities, under
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the educational project of a higher education institution, developed in association or cooperation with one or more R&D units, requirement to be proven at the time of signing the contract. Note: In the case
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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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workplan: ; - Develop the research skills through the application of the selected methods ; - Apply the scientific method on the research process and a critical attitude on the obtained results.; 3. BRIEF
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insurance, supported by INESC TEC. 2. OBJECTIVES: The main goal of this grant is to design a new storage system that leverages heterogeneous storage hierarchies. Specifically, the work to be developed should
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; - Develop research capacity in the areas of data space connectors, interoperability, and open-source repositories. 3. BRIEF PRESENTATION OF THE WORK PROGRAMME AND TRAINING: - Develop tools for the management
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, Biochemistry, and be enrolled in a PhD or a non-degree course, relevant for the development of the activities included in the project. Knowledge in different cell culture models (e.g. cell lines, primary
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- Electrospun Nanofibres for Functional and Customizable Medical Films - CENTRO2030-FEDER-01469100”, funded by the Regional Centre Program (Centro 2030) _PT2030, through the European Regional Development Fund
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plan: The work consists of developing models for the prediction of biological control agents (BCAs), using different approaches: Machine Learning (random forests, support vector machines, lasso), Deep