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Field
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(SDR) platforms and characterise them in the presence of interference in a variety of spectrum sharing scenarios, seeking opportunities for algorithms which provide enhanced interference resilience
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computational social choice, and aims to develop formal frameworks and algorithms for eliciting, aggregating, and analysing stakeholder preferences over risk and safety in AI systems. The Research Assistant will
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and evaluation. The post holder will take a leading role in advancing theoretical and algorithmic research in the domain of probabilistic preference aggregation, contribute to the design and analysis
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systems on software defined radio (SDR) platforms and characterise them in the presence of interference in a variety of spectrum sharing scenarios, seeking opportunities for algorithms which provide
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computational frameworks that combine 4D point cloud data, geospatial analysis, and advanced ML/DL algorithms. Integrate dynamic environmental datasets into immersive and interactive prototypes for scenario
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. The successful applicant will use state of the art inference algorithms to design, use and share the findings of epidemiological models that integrate across large and diverse datasets including capture-mark
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in implementing, testing and validating complex minimisation algorithms that can be used for adaptive trials. Application & interview 8 Experience of collaborating on successful research proposals
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, including: Robot Learning: Creating algorithms that empower robots to learn autonomously from interactions and adjust to new tasks. Manipulation: Enhancing techniques for precise and adaptable object handling
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. In this role, you will be part of the research team, working to develop and evaluate privacy-preserved Generative AI algorithms for generating synthetic Personal Identity Information (PII). This aims
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systems beyond commercially available peptide based systems. A6 Knowledge of data science driven approaches to drug discovery algorithms. For appointment at Grade 8: A4 Some reputation in, and insight