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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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language processing that address concrete problems and are both theoretically rigorous and interpretable. The PhD is funded by the ERC CoG PANDORA (Deep Multimodal Learning for Mining and Generation of Arguments
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methods tailored to ILC. The PhD researcher will fine-tune and benchmark pathology foundation models using multi-site H&E and immunohistochemistry whole-slide images. The aim is to learn representations
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. Kindly submit your CV and motivation letter to the attention of Mr. Menno Koeslag, HR Advisor. Where to apply Website https://www.academictransfer.com/en/jobs/364035/phd-candidate/apply/ Requirements
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-content microscopy and AI/deep-learning-supported image analysis will be used to quantitatively assess neuronal morphology and maturation. Parameters will include dendrite length and branching, axon growth
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, such as assisting in courses of our computing science programmes. Would you like to learn more about what it’s like to pursue a PhD at Radboud University? Visit the page about working as a PhD candidate
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, visiting researchers, master's students, etc.) Research Context Recent advances in mobile robotics have been driven by remarkable progress in perception, deep learning, and control. However, current robotic
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AITHYRA GmbH - Research Institute for Biomedical Artificial Intelligence of the Austrian Academy of Sciences | Vienna, Virginia | United States | about 7 hours ago
Do you want to help transform human health through machine learning and life science approaches? Join AITHYRA in Vienna for a fully funded PhD at the intersection of machine learning, experimental
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archaeological signatures (e.g., micro-relief, edge structures, etc.) – Design and implementation of new deep learning architectures (both supervised and unsupervised/few-shot, 2D and 3D) for an efficient and
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at least one deep-learning framework (PyTorch preferred).•A solid grounding in machine learning. Experience with representation learning, generative models, foundation models or multimodal integration is a