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Field
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, will be employed to identify indicators of track and bridge condition. In parallel, physics-based numerical models will be developed to simulate train–track–bridge dynamic interactions and their
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. The project involves the development and modelling of hydraulic systems, with particular emphasis on energy-efficient system architectures and appropriate component selection. A key aspect is to incorporate
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ARDITI - Agência Regional para o Desenvolvimento da Investigação Tecnologia e Inovação | Portugal | 2 months ago
: planning and execution of oceanographic campaigns; collection, processing, quality control and analysis of oceanographic and atmospheric data; numerical modelling with COAWST and associated tools
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The project unites systems chemistry, advanced microfabrication and quantitative microscopy. It also offers the chance to develop simulation approaches for reaction–diffusion processes to rationalize and
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the area of Computer Science, sub-areas of Artificial Intelligence, Serious Games and Medical Image Processing. The work to be carried out is related to: Design, development and validation of AI models
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a fully operational computing stack with a flexible, renewable-based energy system incorporating both battery and hydrogen storage. Alongside the physical prototype, a comprehensive co-simulation
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are looking for a motivated and talented PhD candidate to join a unique interdisciplinary project at the intersection of machine learning and formal methods. Information Machine learning models deployed in real
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. They are useful for searches of physics beyond the standard model, exploration of many-body physics, and societal applications, such as navigation. So far, clocks operate in a pulsed manner, preparing a
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research experience in electricity and power markets, imbalance forecasting, reinforcement learning, and operations research or optimization. Excellent programming and modeling skills, preferably in Julia
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-Physical Energy Systems The PhD position focuses on the development of secure and trustworthy AI for resource-constrained embedded systems. The research will investigate how machine learning models can be