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The ideal candidate applies machine learning and big data techniques to important questions in economics, combining advanced computational methods with sound economic theory to uncover insights
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computer science / information systems engineering. In this project, the aim is to bridge the gap between large language models (LLMs) and task automation, enabling natural language interaction, that is, via a
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(including Large Language Models and data-driven optimisation) with concrete industrial deployment. A profile comfortable with the challenges faced by industries in their transition towards Manufacturing 4.0
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research in parallel computing, search and optimisation techniques, to provide efficient, scalable and robust solutions to state-of-the-art, large-scale discrete/combinatorial problems. Detailed information
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deploy machine learning and deep learning models (transformers, large language models) for immunological data (biological sequences, single-cell data, and protein structures, virtual drug screening) Use
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use to break into the IT system. Because attack surface became large and diverse [Rizz20], Automated External Attack Surface Management (EASM) cannot be limited to naive IP address scanning or simply
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project will investigate the use of multimodal large language models and fuzzing for metamorphic security testing. The successful candidate will join the SVV research group, headed by Prof. Domenico