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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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4-year PhD fellowship in the Research Programme - Deep Learning-Accelerated Crystallography Pipeline
University. Project description This fellowship forms part of the Novo Nordisk Foundation-funded project “Deep Learning-Accelerated Crystallography Pipeline”, a collaboration between Durham University
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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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robot learning and edge deployment. Closing date: 15 August Overview Robotics is entering a new phase where foundation models connect perception, language and action. Vision-language-action models, robot
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Are you interested in developing mathematically grounded methods for uncertainty quantification in deep learning, particularly for large language models in healthcare applications? Are you looking
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Heidelberg Institute for Theoretical Studies (HITS gGmbH) | Heidelberg, Baden W rttemberg | Germany | about 24 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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–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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awareness These funded PhD scholarships are suitable for students with a background in Computer Science, Mathematics, Engineering and Cognitive Science. Students with interests in machine learning, deep