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learning; distributionally robust optimization; 2) Graph Neural Networks, Large Language Models (LLMs), and geometric deep learning; and 3) federated learning and privacy preserving computing. Basic
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, multimodal, and agentic AI, as well as foundation models, with a focus on geometric deep learning, large-scale knowledge graphs, and large language models. Fellows will also have the opportunity to
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requirements of Monash for PhD (https://www.monash.edu/graduate-research/future-students/apply); Knowledge of machine learning (would be a plus if familiar with NLP and Medical AI); Knowledge of cybersecurity
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at national and international conferences and workshops. Where to apply Website https://www.academictransfer.com/en/jobs/363306/phd-ai-based-cardiovascular-ima… Requirements Specific Requirements You
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models: deep learning (4D CNNs, Transformers, RNNs) on fMRI and ECoG data Explanatory AI: Applying machine learning models to guide post-stroke neurorehabilitation Predictive AI: Optimizing deep
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background in machine learning and deep learning, including experience developing, training and evaluating models, preferably for image analysis tasks. You have strong programming skills in Python
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<span lang="en">Universitätsklinikum Hamburg-Eppendorf</span> | Hamburg, Hamburg | Germany | 4 days ago
(2024) and Cell Reports (2026). Lucia Testa works on geometric and topological deep learning, including neural networks on simplicial and cell complexes, with contributions in IEEE Transactions on
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experience with the PyTorch library and running deep learning models. The successful candidate will work closely with a team of researchers and faculty members in the ClinicalNLP lab led by Dr. Hua Xu
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marine datasets, identifying key process drivers, developing deep learning models for spatiotemporal prediction, and applying them in Baltic Sea case studies. Applicants should have a relevant
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. The project is conducted in collaboration with the deep probabilistic programming group of Thomas Hamelryck : https://di.ku.dk/english/research/groups/machine-learning-in-biology/?pure=en/persons