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PhD Studentship: Distributed and Lightweight Large Language Models for Aerial 6G Spectrum Management
-latency, and scalable operation in aerial 6G networks. In this regard, Large Language Models (LLMs) have recently emerged as a key technology to achieve adaptive 6G spectrum management. The core idea of LLM
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the MicroHH atmospheric model. Analysis of plume dynamics and NOx chemistry in the high-resolution simulations. Develop and refine data-driven methods for emission quantification. Apply your methods to real
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pilots to real-world systems. The overarching aim is to deliver a scalable approach, pairing shared “aggregator” models with household-specific “client” models that exchange knowledge while keeping data
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Large vision–language models (LVLMs) can describe driving scenes and support decisions [Li25], but they sometimes hallucinate objects, relations, or events that are not present [Liu24,Liu25]. In a
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Description PhD Position in Mesoscale and Macro-homogeneous-scale Modelling of Electrodes 100% ZHAW - Zurich University of Applied Sciences Winterthur, Switzerland This doctoral thesis is part of the European
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along with physiological and behavioural data of test drivers and build explainable predictive models out of it. The research team spams Luxembourg, Europe and the USA and will make use of world-leading
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vehicle. Multiscale simulations of the downstream expansion behaviour of the residuals for different atmospheric layers. Definition of suitable interfaces to process data for the climate models. Expected
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modelling for the sources of fine particulate matter (PM2.5) at urban sites in the UK’, Atmospheric Environment, 343, p. 120963. Defra Air Quality Expert Group (2017) The potential air quality impacts from
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research. The student will work closely with colleagues in the topical area of Atmospheric and Climate Physics (ACP). The successful candidate will have opportunities to present their work at international
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to changing internal states and external environmental conditions. Both traditional model-based approaches and modern learning-based control techniques will be employed to achieve an appropriate trade-off