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aims at developing tools using large language models (LLMs) for the correction of misinformation about climate change in social media. The successful candidate will develop innovative tools leveraging
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model of human working memory. During the project, we will follow an intertwined model-experimental approach. First, we will use electroencephalography (EEG) experiments to investigate the neural basis
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will use computational models to explore the minimal functional requirements for self-replication to emerge from polymerising molecules. Instead of simulating specific chemistries in full detail, we will
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properties of catalysts together with statistical methods to derive predictive models for selective catalysis. In a data-driven approach, an initial set of reactions is analyzed and used to establish such a
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advanced operational strategies, such as model-predictive control, tailored to dynamic prosumer energy demand. Foster collaboration: Work closely with industrial and research partners, including CENAERO
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models combined with the finite element method. Constitutive relations are required to describe material behavior. Advanced stainless steel typically possess complex microstructures across various length
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models combined with the finite element method. Constitutive relations are required to describe material behavior. Advanced stainless steel typically possess complex microstructures across various length
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) developing and validating preprocessing pipelines; (3) architecting and comparing spectral-only and multimodal (HSI + NIR + Raman + RGB) deep-learning models; (4) implementing robust sensor-fusion strategies
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problems. The mission of this consortium is to realize a new revenue model in the textile value chain, by scaling up and strengthening the (business/government/NGO) activities around sorting and recycling
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population projections and management of wild bird populations in times of climate change”, with the Seychelles warbler (Acrocephalus sechellensis) as a model system. The project is supervised by Prof. Hannah