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adaptability, and safety; Applying AI and optimisation techniques (e.g. reinforcement learning and evolutionary algorithms) to adapt locomotion strategies to varying surface conditions; Supporting
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gene gain/loss events, horizontal gene transfer, and functional diversification within gene families. You will apply statistical models and machine learning algorithms to identify associations between
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) section. The BEE section investigates ecological and evolutionary patterns and processes underpinning biodiversity, scaling from genes to communities and ecosystems, and how these are affected by
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range of disciplines, including evolutionary biology, ecology, computational biology, genetics, and comparative genomics. The build-up of biodiversity gradients from spatial diversification dynamics 1
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and iteratively improved. • Integrate and test autonomy stacks (perception, learning, planning) on physical robots. • Use evolutionary algorithms to optimize both the robot’s body and brain
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Offer Description The researcher will develop various applications, algorithms, and AI techniques for Virtual Power Plants (VPPs) within the distribution grid environment. These will include neural
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Assessment Systems: Toward Trustworthy AI for Complex Educational Evaluation Image and Video Analysis Using Machine Learning Algorithms Mathematical and Computational Neuroscience, from neural data and network
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-including evolutionary algorithms, ant colony optimisation, and simulated annealing-to fine-tune an LLM/agent that generates high-quality prompts, inputs, and tool-use strategies for density functional theory
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of areas, including AI and machine learning, cloud and mobile computing, computer system and information security, evolutionary computation, computer vision and graphics, and bioinformatics
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., evolutionary algorithms/strategies, mixed-integer search, multi-objective methods). Strong Python and scientific-computing skills (data handling, experiment tracking, testing, version control). Practical