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Field
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on large annotated datasets. Memory-efficient deep learning: Model compression, pruning, quantisation, selective memory replay, and efficient training strategies. Energy-efficient deep learning: Methods
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PhD project, the successful candidate will develop an open-source workflow using deep learning and hierarchical statistical models to streamline the data flow from acoustic recorders to ecological
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aspects of machine learning. Applications include improving the efficiency of data assimilation methods and understanding why and how deep learning works. Applicants should have, or expect to achieve
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aims to characterise the sequence, structural, and functional properties of UL-CDRs using deep learning and structural bioinformatics, with the goal of identifying novel antimicrobial peptide candidates
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imaging using deep learning. You will study the water imbibition in hierarchically porous Si‑based material systems across multiple length and time scales. These systems can manipulate fluid transport
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biologically-inspired deep learning and AI models (NeuroAI). The computational models we work with include vision deep learning models (including topographical deep neural networks), multimodal vision and
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maintenance. Development of machine/deep learning methods to detect fault, provide early warning and reporting, and forecast lifetime trend of batteries, to support predictive maintenance and improve energy
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Grant Arts PhD Fieldwork Grant Application is required. Check eligibility Key scholarship details Application status Open for applications Benefit amount Up to $12,000 Eligible study level Graduate
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Location: Central Cambridge PhD Studentship - Marie Curie network ON-Tract: Protein engineering of enzymes: in vitro directed evolution and machine learning-based elaboration of biocatalysis
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. To learn how to apply, go to the How to Apply page: westminster.ac.uk/research-degrees/how-to-apply . For useful resources to support your application, visit the Thinking of Doing a PhD page