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PhD studentship: Discovery of rational therapeutic biomarkers in breast cancer by systems pathology and deep learning Supervisor: Dr Hamid Raza Ali Department/location:Cancer Research UK Cambridge
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25th September 2026 Languages English English English The Department of Marine Technology has a vacancy for a PhD Candidate in Deep Learning Enhanced FSI analysis of Modular Floating Structures PhD
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PhD Studentship: Efficient Long-Horizon Task Execution in Physical AI (deep learning, computer vision, robotics) Number of awards: 1 Award information: Fully funded PhD studentship covering Home
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and large models, limiting real-world deployment. This PhD focuses on efficient Physical AI, emphasising data-efficient training, reinforcement learning, continual adaptation and edge deployment
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to predict marine extremes for engineering and navigation purposes The PhD candidate is expected to develop an integrated framework that combines machine and deep learning methods with statistical and
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Heidelberg Institute for Theoretical Studies (HITS gGmbH) | Heidelberg, Baden W rttemberg | Germany | about 14 hours ago
) in Geometric Deep Learning to join the Machine Learning and Artificial Intelligence (MLI) g roup to perform research in geometric deep learning for materials science. This research is part of
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, the ability to analyze the full dataset collected by the experiment will be severely limited. The L2IT is a leader in developing new track reconstruction algorithms using geometric deep learning methods
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one or more of the following areas is meriting: Bayesian statistics, mathematical modelling, probabilistic machine learning, deep learning, large language models. Rules governing PhD students are set
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–2 years, total 3–4 years) on deep learning for medical imaging. This DFG-funded project focuses on developing deep learning methods for medical and scientific imaging. The Professorship for Machine
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PhD Scholarship Develop AI and machine learning models to guide real-time, personalised treatment of paediatric brain cancer using multiomics and clinical data, within the internationally