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on increasing variability and extreme events in watersheds. Expand knowledge of process-based modeling approaches to assess relationships between forest species composition, biomass, and water yield Develop
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. Existing methods rely on fixed data and static models, which struggle to adapt to real-time changes and unpredictable conditions. This limits the ability to optimize energy storage use for critical
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the complexity further to effectively plan their movement and deployment. Existing methods rely on fixed data and static models, which struggle to adapt to real-time changes and unpredictable conditions. This
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. Going beyond the canonical sub-Gaussian noise models, the objective is to prove tight convergence rates for first-order or zeroth-order methods when the noise is heavy tailed. This allows us to reliably
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machine learning, big-data analytics, and data-driven approaches to optimise composition–process–property relationships. Key responsibilities will include: Research: Conduct additive manufacturing research
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Composition: President: Hugo Agostinho Machado Fernandes Effective Members: Cláudia Maria Carrudo de Deus; Sandra Sofia Mimoso Pinhanços Alternate Members: Cristina Blanco Elices; Luís Filipe da Silva Ribeiro
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laboratory experience in areas relevant to the project, namely: needs diagnosis and assessment; development of artificial intelligence-based forecasting models; and development of optimal control models
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minutes, and published on https://apply.uc.pt/ . VII.IX -Jury Composition: President: Paulo Jorge Rodrigues Amado Mendes Effective Members: Luís Manuel Cortesão Godinho and Andreia Sofia Carvalho Pereira
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matter’ of biology, under-studied owing to the historical lack of preparative and analytical tools to probe the local molecular composition and transient interactions of molecules within glycocalyces, and
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modelling. You will be self-motivated with a proven track record on molecular and microstructural analysis, waste valorisation with stabilisation, degradation kinetics & durability and engineering knowledge