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, the successful candidate will be appointed as either a Lecturer (Teaching) or a Senior Lecturer (Teaching). Key responsibilities: Design and deliver innovative teaching and learning materials, including blended
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from Deepfakes Project. We are looking for a software/machine learning engineer (or similar) to work in an interdisciplinary team reporting to Dr Sophie Nightingale (Principal Investigator). The Project
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focus will be on biomechanics, image processing, machine learning (ML), artificial intelligence (AI), and metrology, the student will also contribute to the co-design of cadaver experiments and data
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tools, collaboration with project stakeholders, and engagement with the consortium and Defence and Security stakeholders. Technical Requirements: Strong coding skills with background in machine learning
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omics data, digital pathology, using cutting-edge AI and machine learning approaches. You will play a critical role in developing sophisticated computational pipelines, integrating complex multi-omics
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at leading international conferences and publish in top-tier journals. The successful candidate will gain advanced expertise in multi-sensor fusion, signal processing, machine learning, and positioning
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may also explore embedding these new computational methods into optimisation and machine learning contexts. The new computational techniques developed will be geared towards the following key
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. Fe, S) on CNT purity and structure. Evaluate CNTs as conductive additives in standard Li-ion battery electrodes. Apply AI/machine learning to optimise experimental design and growth parameters
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technologies Artificial Intelligence and Machine Learning applications Computer Vision and sensor integration for agricultural applications Human-Robot Interaction in agricultural contexts Robotic Perception and
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observation-based climate datasets. In addition, we will also use innovative machine learning tools to evaluate the relationship between a set of hypothesised climatic precursor conditions, called (potential