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Field
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solutions using first motion. Experience in full waveform inversion using innovative tools (e.g. ISOLA) and methods (ML), earthquake location algorithms, computer programming and geophysical equipment
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. Expected Work: The selected candidate will conduct research at the intersection of wireless communications, signal processing, and control theory, aiming to develop cutting-edge algorithms for joint
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algorithms. Investigate the use of LLMs as agents or interfaces for querying building data and optimizing control parameters. Validate methodologies using real-world data from built environment monitoring and
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conducted to assess the feasibility of prototypes in terms of activity tracking, updating game elements, and visualisation capabilities. The second objective of the thesis is to develop models and algorithms
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algorithms for resource-efficient learning, for example via data selection and filtering (leveraging that not all data is equally informative). You will also investigate complementary approaches that reduce
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address domain-specific challenges in biomedical applications. The PhD project will focus on translating and enhancing cutting-edge algorithms from AI research into concrete applications in biomedical
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on high-fidelity modelling and test data for both metals and thermo-set composite materials. To achieve this we will explore the use of advanced genetic algorithms and/or Artificial Intelligence (AI
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information sources and to provide the relevant analysis of all the available variables in different scenarios conditions. In order to reach this goal, deep learning-based algorithms will be implemented
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functional theory. In collaboration with Phasecraft, a leading quantum algorithms company, this project will explore the generation of new quantum computing datasets and the development of machine learning
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precision medicine based on gene sequencing time series data. Large data sets come with significant computational challenges. Tremendous algorithmic progress has been made in machine learning and related