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-specific challenges in deploying distributed AI for power system control. The outcomes will be invaluable to electricity system operators, flexibility aggregators, and the broader energy research community
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-specific challenges in deploying distributed AI for power system control. The outcomes will be invaluable to electricity system operators, flexibility aggregators, and the broader energy research community
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. Development of multimodal AI models that fuse data from multiple types of sensors to accurately model and predict wind turbine blade damage. Establish and develop data science pipelines for wind turbine blade
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interventions are maintained in the context of stressors. In order to examine these questions, the project will use multiple methods such as experience sampling, laboratory tasks, and a randomized-controlled
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, can play a vital role in creating inclusive learning environments but might lack diverse perspectives. This limits opportunities for students to engage with multiple viewpoints and hinders epistemic
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”, led by Associate Professor Valeria Vitelli. Successful candidates will work on Bayesian models for unsupervised learning when multiple data sources are available, mostly tailored to the case
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the molten salt/metal interface at multiple scales, from atomistic to mesoscopic, with the goal of identifying the fundamental mechanisms driving corrosion. Moreover, you will use this knowledge to suggest
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chemistry, theory and chemical engineering, investigate how solvents play an active roll in the control, mediation and regulation of chemical reactions and processes. RESOLV’s mission stretches from
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dynamic research environment. Strong organizational skills and ability to manage multiple tasks and deadlines effectively. Adaptability and openness to learning new methodologies and techniques. Commitment
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from multiple academic disciplines and a dialogue between the sciences and humanities. The Doctoral Programme in Environment and Society promotes both disciplinary depth and interdisciplinary approaches