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, Reasoning and Validation (Serval) research group and work on a research project related to the application of machine learning for official statistics. The subject of the thesis will be “Exploring Large
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data-driven methods (optimisation, generative AI, agent-based modelling, machine learning). Our work provides decision support for policy makers, industry stakeholders, and researchers by delivering
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machine learning, deep learning, or computer vision Experience with Python and common AI frameworks (PyTorch, TensorFlow) Interest in hallucination detection, robustness, trustworthiness, and (optionally
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topics: Software engineering practices for machine learning Tabular machine learning Large language models on structured data PhD Student Role: Under the direction of their supervisor, the candidate will
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machine learning or trustworthy AI, including experience with robustness assessment and attack/defense mechanisms. Expertise in software security and code analysis, with understanding of common
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mechanistic studies of microbiome-mediated pathogenesis. This is achieved by bridging microbiology and big data analytics in a structured doctoral training environment. The need of microbiome research in
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of metabolic network modelling linked to epigenetics Carry out machine learning, and integrative analysis of large epigenome datasets Communicate research results in international conferences and journals Work
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achieving lower system costs. More information on the project: https://radarmimo.com/4d-imaging-automotive-mimo-radar/ The selected PhD candidate will work on one of the following research topic: Optimal
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framework to bridge this gap and enable organizations to confidently deploy secure GenAI solutions by evaluating the machine-learning models intrinsically, identifying components of an AI pipeline and their
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activities across these decentralised and increasingly complex networks. By deploying and advancing techniques such as machine learning, graph-based network analysis, and synthetic data generation, the project