238 algorithm-development-"The-University-of-Edinburgh" Fellowship positions in Portugal
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with regard to the tasks: “Exploiting Monte Carlo simulations for accurate dosimetry assessment”, and “Developing a multi-scale model to correlate micro- and nanodosimetric patterns of radiation (sub
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informatics or related field, or in non-degree course developed in association or cooperation between a High Education Institution and one or multiple research units, namely in the area of informatics or
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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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Computer Engineering, Physics, or related areas. The goals of the grant are: Design, develop, and validate the web platform and data pipelines that support the end-to-end flow of the digital twin, including
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-scaling architectures; - identify and select methods for researching, developing and validating the target topics; - develop research and prototypes using complex real-world architectures as case studies
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | about 1 month 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