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Field
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learning, algorithms, and programming. Prior exposure to reinforcement learning or human-robot interaction is highly desirable, though motivated candidates with a strong grounding in AI/ML and willingness
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Species’ distributions are shifting in response to global climate change and other human pressures. Accurate methods to monitor and predict distribution shifts are urgently needed to manage
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formative assessment and personalised feedback while ensuring fairness, accountability, and transparency. The research will explore a combination of algorithmic design, human–AI interaction, and empirical
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Performance . About You The successful candidate will play a key role in the development and validation of computational tools that integrate spatial transcriptomics, algorithmic methods, and machine learning
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computational tools for quantifying electromagnetic field distributions down to the fundamental atomic scale. The project will build on recent developments in inverse scattering methods, including ptychography
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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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. Working closely with project teams, architecture functions, and geographically and technically distributed teams, the M365 Technical Lead will help shape and grow organisational capabilities within
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theory, ergodic theory, differential geometry, data science, and/or machine learning. Implement algorithms that efficiently analyse dynamical systems arising from idealised models or data. Collaborative
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modelling and simulation of transmission and distribution networks, including benchmarking data models, developing optimal power flow algorithms, and creating state estimation and multi-energy optimisation
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planning algorithms for GPS-denied lunar environments and extreme operational conditions stochastic optimisation frameworks for mission-critical decision-making under uncertainty Research areas and technical