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Field
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The PhD position is part of the MSCA Doctoral Network PANDORA on discovering Pathomechanisms and Novel Drug Optimization for AMD (Age-Related Macular Degeneration). AMD is the most common cause of blindness
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sustainable future, including but not limited to, studies related to new technologies, process integration and policy measures, as well as overall energy system aspects at local, national and supranational
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of software infrastructure to optimize data workflows Developing analytical methods to integrate different types of sequencing-based DNA data. Developing computational models for prognosis and treatment
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probabilistic and deterministic reliability criteria with market operation in a single security-constrained optimal power flow model, and combining optimal power flow analysis with reliability analysis methods
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for three consecutive periods (2014-2018 and 2018-2022 and 2023-2026). ICN2 comprises 20 Research Groups, 7 Technical Development and Support Units and Facilities, and 2 Research Platforms, covering different
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largely by the rapid expansion of artificial intelligence (AI), cloud computing, and high-performance data processing applications. As AI models continue to increase in size and computational complexity
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-time operation. The project will focus on how AI and mathematical optimization can be combined to support sequential bidding decisions under uncertainty. The initial use case will consider a wind power
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ingredients, a process that is traditionally slow because each substrate–strain combination behaves differently. By applying machine learning to historical experimental data, we can predict high‑potential
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. The focus of this PhD position is the development, characterization and experimental evaluation of advanced sensing hardware. Signal processing and Artificial Intelligence are applied as enabling technologies
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at different stages of the solving process. (2) A set of algorithms for integrating domain knowledge before optimization as priors and after optimization in the form of structured expert feedback, with