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on developing the imaging system as well as novel machine learning approaches for image analysis and disease classification using field data from German and Brazilian agricultural trials. Responsibilities Design
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) data. We also analyse macaque electrophysiology data obtained through collaborations. We use machine learning techniques for data analysis and computational modelling with a special interest in
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. The research in the PhD project will focus on core spatio-temporal machine learning method development, including: generative models for grid-based and particle-based spatio-temporal data; controlled generation
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-temporal machine learning method development, including: generative models for grid-based and particle-based spatio-temporal data; controlled generation methods for data assimilation; and graph-based multi
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About the project: Machine Learning to Unlock the Mysteries of Metallic Phase Transitions Supervisor: Dr Livia Pártay, University of Warwick Join a PhD project that goes beyond state-of-the-art to
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Details Title Postdoctoral Fellow in Computer Science — From Theory to Practice: Reinforcement Learning for Large Scale Foundation Model Post‑Training School Harvard John A. Paulson School of
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and model human behaviours across diverse socio-contexts. Objectives: Identify and curate multimodal datasets representing varied socio-contexts. Develop robust context-aware multimodal learning methods
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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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, and space hardware. This PhD research aims to develop a comprehensive Mode Selection Framework for Reduced Order Modelling (ROM) in Structural Dynamics—using machine learning to build robust
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to 1) Object-attribute compositionality to replace exhaustive data requirements with structured concept learning, 2) Bias detection and machine unlearning to identify and mitigate bias and shortcuts