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, “Instituto de Engenharia de Sistemas e Computadores: Investigação e Desenvolvimento em Lisboa” is a Research and Development and Innovation Organization (R&D+i)) in the fields of Computer Science and
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– “Scientific and technological synergies for sustainable development of the wine chain in the North region”. With the reference NORTE2030-FEDER-01786100, co-financed by the Northern Regional Program 2021-2027
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Infrastructure? No Offer Description A call for application is now open for the attribution of 2 (two), with reference 81/2025/BI/AgendasPRR, for development of scientific and technological research activities
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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
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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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Portugal and Brazil can provide a more accurate parametrization of global integrated models and support the development of sustainable food and environmental policies. Examines the impact of the variation
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://drh.tecnico.ulisboa.pt/files/sites/45/despacho_8532_regulamento_bolsas.pdf Workplace: The work will be developed in the Content and Media Office (NCM) of the Communication, Image and Marketing Unit (ACIM) at Instituto
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related areas. Objectives: This scholarship is part of the GreenSET project, which aims to develop a new technology for the production of epoxy-based biocomposites using only monomers from renewable sources
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of this type of scholarship (whichever comes first), with a planned start date in October 2025. .WORK PLAN: In this context, the aim is to develop, implement and maintain aquaponic systems using intelligent
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 28 days ago
). Develop an accompanying uncertainty‑quantification (UQ) layer on top of EM to produce calibrated credible/prediction intervals. Train, validate, and stress‑test on ECoG data, benchmarking against