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Project summary: This PhD develops and evaluates a GPT-based AI co-facilitator for Group Model Building (GMB) workshops, supporting stakeholders in constructing Causal Loop Diagrams of complex socio
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development of the LOTUSIM-Energy simulator and its associated autonomy components. Their main responsibilities will be: - Developing and integrating multi-physics simulation models for maritime, aerial, and
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individuals and as communities comprising larger ecosystems. Traits are also often used as parameters in computer models of terrestrial ecosystems and even the entire Earth System (such as climate models used
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models for high-dimensional and functional data ”, led by Professor Valeria Vitelli. Successful candidates will work on Bayesian models for unsupervised learning when multiple data sources are available
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include, among others: Read scientific literature in the field of quantum communication and quantum key distribution (QKD) systems. Mathematical modeling of the quantum signals emitted by a QKD transmitter
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translational science, leveraging molecular, cellular, and animal model systems as well as patient-derived samples to uncover mechanisms of disease heterogeneity and progression and identify novel therapeutic
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Water consumption under various application parameters Impact of dosing changes on crop quality Economic efficiency Impact on variability of soil properties Predictions based on AI models Supervisor
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defines the basic properties and differences of VR/AR, describes their advantages and disadvantages, as well as their practical use in industry. The doctoral student will design three-dimensional models
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and the safety implications arising from these interacting processes. You will use and extend PyBaMM, which is an open-source Python-based battery modelling framework (https://pybamm.org