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to characterize forests and their biodiversity. The research will focus on developing multimodal learning approaches that combine complementary forest information across data sources, spatial scales, and time. A
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19th October 2026 Languages English English English The Department of Electric Energy has a vacancy for a PhD Candidate in Risk-Based Power System Operation and Utilisation of Transformer Capacity
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, or characterizing synthetic and manipulated visual content. Study representations learned by large pretrained visual or multimodal encoders, including probing, adaptation, fusion, and efficient fine-tuning strategies
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materials for hydrogen energy applications. Hydrogen is considered as a clean alternative to fossil fuels in heat and electricity production. However, significant knowledge and technical gaps must be
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learning will be explored as enabling technologies for automated leakage detection and localization, analysis of complex measurements, fault-response characterization, intelligent exploration of large
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fossil fuels in heat and electricity production. However, significant knowledge and technical gaps must be addressed before the transition from fossil fuels to hydrogen can take place. This PhD position is
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technologies for automated leakage detection and localization, analysis of complex measurements, fault-response characterization, intelligent exploration of large experimental spaces, and adaptive selection
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of the candidate is to fabricate and characterize wearable optical metasurfaces and photonic crystals. Main focus will be the understanding of the complex interactions of light with photonic devices as
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characterization techniques. The successful candidate will work closely with other researchers and industrial stakeholders to contribute to the development of more sustainable flotation strategies for future mineral
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Perovskite oxides (magnetic oxides) to fabricate emerging interface properties. The position will focus on the electronic reconstruction of the interface, developing ways to characterize the topological