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motivation for pursuing a PhD trajectory is essential, as this path involves unique challenges and uncertainties inherent to scientific exploration. Success requires dedication, adaptability, the ability
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future scenarios, including climate change, energy and resource use, water consumption, waste generation, and emissions, while conducting sensitivity and uncertainty analyses to assess the robustness
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under geopolitical and environmental uncertainty. The successful candidate will join an interdisciplinary research environment working at the intersection of finance, sustainability, and industrial
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SD-26109- POSTDOCTORAL RESEARCHER IN AI-BASED ENERGY MANAGEMENT OF RESILIENT MICROGRIDS WITH SECO...
our website: https://www.list.lu/ How will you contribute? You will contribute to the ENERGY-RESILIENT-UA project, which aims to support vulnerable communities in Ukraine through resilient, low-carbon
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to validate the proposed stochastic modelling framework. By capturing uncertainty and variability more rigorously, the project aims to significantly improve the predictive accuracy of models used to assess
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. There is also a need for accounting noise in Deep Learning models and quantifying uncertainty in real-world applications. This PhD studentship (scholarship) leverages large-scale Earth Observation data
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about uncertainty processing across these different scales. At later stages of the project, the role holder will have the opportunity to work with co-Is responsible for climate modelling and longitudinal
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multiple techniques such as onboard safety monitoring, operating environment adaptation and real-time robust learning of uncertainties and nonlinearities within the dynamical system. In this PhD, the aim is
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ARDITI - Agência Regional para o Desenvolvimento da Investigação Tecnologia e Inovação | Portugal | 2 months ago
summarily excluded. The President also has the right to require any candidate, in case of doubt and for the purposes of admission to the selection procedure, to present supporting documents
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. Particular emphasis will be placed on understanding how observation systems respond to environmental stress, uncertainty, and increasing data complexity, as well as how monitoring approaches can be designed