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unit and then pre-processed data used as the input of the deep learning algorithm. The research will employ the SafeML tool (a novel open-source safety monitoring tool) to measure the statistical
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. Currently, patient selection is insufficiently accuracy, our preliminary modelling work suggest that biomechanics modelling can improve this. You will work with clinicians across Europe to test your algorithm
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quality, avoiding the paralysis that troubles artificial algorithms when options seem equally good. This project asks: what objective functions do such biological systems optimise, and how can we use
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cutting-edge computer vision technology. Working closely with the Principal Investigator, Co-PI, and interdisciplinary research team, RE will develop and implement deep learning algorithms to analyze trap
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predictive performance, computational efficiency, and spatial resolution through algorithm optimisation, tuning, and refined covariates. Assess trade-offs between spatial resolution and other performance
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practice while supporting complex tasks such as image and video enhancement, layered editing, and object-level content manipulation. The candidate will contribute to 1) algorithmic development, including
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harness advanced techniques such as machine learning, optimization algorithms, and sensitivity analysis to automate and enhance the mode selection process. The result will be a scalable methodology that
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algorithms that allow robots to refine their control strategies based on observed human behaviour. Collaboration: The project will benefit from extending existing collaborations between the University
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transport calculations and programming. Expertise in quantum transport and using machine learning algorithms for first principle calculations will be distinct advantage. You should be able to work
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programming skills. Expertise in developing computer vision and machine learning algorithms would be desirable, highly motivated and enthusiastic about advancing AI for societal impact. Qualifications A high