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4-year PhD fellowship in the Research Programme - Deep Learning-Accelerated Crystallography Pipeline
determination by developing mathematical methods and integrating machine learning into crystallographic workflows. The successful candidate will develop theoretical and computational approaches to improve
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via feedback, interaction and continual learning. It will explore converting human videos, simulation, web-scale data and unstructured experience into supervision and reward signals through relabelling
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estimated geological CO₂ storage capacity, with the Southern North Sea (SNS) representing one of its most strategically important regions. While depleted gas fields are already being developed for carbon
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prediction to systems that reason, plan, interact and act in the physical world. This PhD addresses efficient long-horizon task execution in Physical AI—complex tasks needing sequences of decisions, subgoals