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technologies, ethical implications, and governance frameworks, including knowledge of algorithmic accountability and transparency. Experience with both qualitative and quantitative research methods, and
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are in particular targeting development of data-driven high-performance computing techniques for unbiased discovery of generative models & theory and algorithms for network inference with special reference
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) you wish to be considered for in your cover letter. Job Purpose You will contribute to/make a leading contribution to a project EPIC (the Scottish Government funded Centre of Expertise on Animal Disease
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conditions. Our work combines traditional statistical methods with advanced artificial intelligence algorithms to identify patterns in disease. We also use qualitative methods to understand lived experiences
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interaction and analytics using AI. Key Responsibilities: Development of artificial intelligence (AI) technologies to perform human-robot interaction and analytics System integration of the developed algorithms
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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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cooperative, competitive, and mixed settings. Collaborative decision-making frameworks and decentralized learning algorithms. Adaptive, meta-learning, and context-aware strategies to enhance policy
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to research-based activities, including the development of new data analysis algorithms, processing and analysis of field data, and participation in the fieldwork. Your responsibilities will include: Conduct
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and planning algorithm for high-speed autonomous vehicle. The work will involve algorithm development, simulation, test rig set-up, and experimental validation. Requirements: The candidate must at least
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development team Interacting with Continental business areas Developing and integrating AI algorithms into the real development progress Preparing academic publications such as patent applications and research